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This is to certify that the

thesis entitled

The Effects of Goal Type and Metacognitive
Training on Complex Skill Acquisition:
Implications of the. Limited Resources Model

presented by

Daniel Adam Weissbein

has been accepted towards fulfillment
of the requirements for

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M.A. degree in sychology

 

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TO AVOID FINES return on or before date duo.

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THE EFFECTS OF GOAL TYPE AND METACOGNITIVE TRAINING ON
COMPLEX SKILL ACQUISITION: IMPLICATIONS OF THE LIMITED
RESOURCES MODEL
By

Daniel Adam Weissbein

A THESIS
Submitted to
Michigan State University
in partial fulﬁllment of the requirements for the degree of

MASTER OF ARTS

Department of Psychology
1996

 

ABSTRACT
THE EFFECTS OF GOAL TYPE AND METACOGNITIVE TRAINING ON
COMPLEX SKILL ACQUISITION: IMPLICATIONS OF THE LIMITED
RESOURCES MODEL
By
Daniel A. Weissbein

Research on the limited resource model of complex skill acquisition has
suggested tint goals early in skill acquisition may be detrimental to learning and
performance beeause the resultant self-regulation competes for cognitive resources.
However, past research has examined primarily performance goals. This study
examines whether learning goals sequenced in accordance with hierarchical sequencing
theory and/or metacognitive training allow learners be more efﬁcient with their
cognitive resources leading to increased learning, and better performance. A
conceptual model is offered to examine the impact of goal type and metacognitive
training on learning and performance, mediated by metacognitive and learning activity.
Themodelistestedwitha 2(sequencedsubgoals)x2(pmenceorabsenceof
metacognitive training) design, analyzed using hierarchieal regression. The
manipulations were generally unsuccessful at increasing metacognition or learning
activity, nor did they increase learning or performance substantially. Metaeognitive
ratings were the most promising way to measure metacognition. Ability and goal
committment, led to knowledge aquisition which predicted performance. Metacognition
related to knowledge acquisition Implications and future directions are discussed.

For my Sister, Sarah.

iii

 

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ACIQIOWLEDGEMENTS

This Master’s Thesis would not have been possible without the patient
coaching, editing, encouragement, and motivation ﬁom my committee Chair, J. Kevin
Ford (O.K., perhaps possible, but not bearable). I thank goodness that his thinking is
so much clearer than his handwriting. I'd like to apologize to Kevin for not putting
pagenmnberonthedraﬁs, itwasmerelyanattempttocreateadiversionsosomeof
mymistakesmightslipbymmoticed Itistohiscreditthatsofewdid

I‘d like to also extend thanks to my committee members, Daniel Ilgen and Rick
DeShon. They Ind to wade through the introduction and method sections not once but
twice! I apologize to them as well. (Their copies had numbers, but as they remained
awakelateatnighttoﬁnishthedraﬁs,theymusthavefeltthatthesemmrbersran
distressingly high) Their comments not only improved the dralt, but made me feel a
lotbetterabouttheresults. Itistotheircreditthatthedefenses ofthisthesiswere
challenging but always had a collegial and developmental tone. I can tell them now
that I secretly enjoyed the defenses. Oh, maybe I shouldn't write that before my
dissertation - really, guys, the process was hell and I was gladjust to make it out
alive!

Thanks also go to the TIDE2 lab directors John Hollenbeck and Dan Ilgen
(again) who funded me for two ofthe years I worked on this project. Not only have I

iv

learnedalotaboutteamresearchﬁ'omthem, theyalsoﬁmdedmetohangoutin
Tiajuana! (Actually I presented a paper in San Diego, so there's no story there,
Geraldo.)

Acknowledgement also to the innumerable people in addition to those above
who challenged me, taught me, and helped me develop my understanding about self-
regulation and learning. Those to whom I am most indebted are: Ken Brown, David
Chan, Stan Gully, Steve Kozlowski, Eleanor Smith I'd also like to thank Mary K
Casey for putting up with my venting about this thesis, as well as her support, butt
kickings, and steady supply of the best candy in the world (Swedish Fish, for which
I'd also like to thank all the brilliant confectioners of Sweden).

My parents, of course, deserve more acknowledgement than an
acknowledgement section can acknowledge. Not only did they foster my love of
edueation (read: bribed me for getting A's in school), but they also did not disown me
when they realized that I chose to go to The George Washington University for my
bachelor’s degree. I mean, what else would they have done with $80,000? Don't
answer that Their never ending love and attempts to understand what exactly an I/O
Psychologist does have been very inspiring and comforting. ll promise that I will tell
them what an I/O Psychologist does as soon as I figure it out. I also thank them, as
well as my brother and sisters for setting the bar so high I am sure this degree, and
many years of intense counselling, will help me to overcome my inferiority complex
when I corrrpare myselfto their achievements. Thanks to Cynthia for trying to stop
meﬁ'omcomingtogradschool— shewascorrectlwas indenial ofhowhardit

would be. Thanks to Sarah and David for choosing Occupational Therapy and
Aerospace Engineering as their ﬁelds, not I/O Psychology.

Thank you to the New York Giants for keeping me from working on this
Thesis on Sunday afternoons. I only wish their defenses would go as smoothly as
mine did

Kudos to all researchers who have labored to understand metacognition and

self-regulation. I feel your pain.

vi

 

r J

 

TABLE OF CONTENTS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES .............................. - ix
LIST OF FIGURES ............................... - x
INTRODUCTION - - - - - 1
Traditional Approaches to Skill Acquisition ................. 3
Cognitive Factors Inﬂuencing Skill Acquisition ................. 9
Cognitive Resources ......... 9

Cognitive Abilities ....................... 12

Motivation - .......... 16

Self Eﬂicacy .............. 17

Self-Regulation .............. 19

Limitations of Skill Acquisition Literature ............................. 26

A Training Perspective - -- ........ 30

Using Subgoals to Sequence learning ................. 31

Metacognition 37

A Conceptual Model -- ........................ 43

On Task Resources - ...... - 45

Learning Activity ......................................... 46

Self-Regulatory Activity ............................. 48

Linkages _ ........................ 51

Hypothesis 1 ...................... 55

Hypothesis 2 ............... 57

Hypothesis 3 ..................................................... 58

Hypothesis 4 -- ............ 60

Hypothesis 5 .......... - - .... 62

Hypothesis 6 -- .. 62

Hypothesis 7 ..... - 63

Hypothesis 8a & b ........... 64

Hypothesis 9a & b ........... -- 65

METHOD ........................................................... 66

Sample and Design ................................................................. 66

Task ...................................................... 66

Procedure .............................................. 67

 

vii

 

Goal Type Manipulation .....................................................
Metacognitive Training .....................................................
Pilot Studies .............................................................................

RESULTS .........................................................................................
Descriptive Data .................................................................
Sequenced Subgoals and Leaming Activity .................
Metacognitive Activity and Training .............................
Knowledge and Performance .........................................
Tests for Mediation .................................................................

DISCUSSION . . .............................................................................
Knowledge .............................................................................
Theoretical Considerations .....................................................
Implications and Future Directions .........................................

LIST OF REFERENCES .................................................................

 

Appendix D: Metacognitive Training . .........................................
Appendix E: Metacognitive Activity Questionnaire .................
Appendix F: Goal Directions .....................................................
Appendix G: Goal Commitmmt Questionnaire .............................
Appendix H: Knowledge Test .....................................................

Appendix 1: Learning Activity QJestionnaire .............................

Appendix J: Post Scenario Questionnaire .........................................
Appendix IQ Debrieﬁng Sheet .....................................................

Appendix L: Instructions to Raters .........................................
' M: Mediation Analyses: MC Training on Knowledge .....
Appendix N: Mediation Analyses: MC Training on Performance

Appendix 0: Mediation Analyses: Goal Type on Knowledge

Appendix P: Mediation Analyses: Goal Type on Performance .....

viii

Table 1:
Table 2:
Table 3:
Table 4:
Table 5:
Table 6:
Table 7:
Table 8:
Table 9'

Table 10:
Table 11:
Table 12:
Table 13:
Table 14:
Table 15:
Table 16:
Table 17:
Table 18:

LIST OF TABLES

Study Hypotheses and Analyses .............................
Means, Sds, and Reliabilities .............................
Correlation Matrix .....................................................
Hypothesis 1 .................................................................
Hypothesis 1 .................................................................
Hypotheses 2, 3, and 4 .........................................
Hypotheses 2, 3, and 4 .........................................
Hypotheses 5 and 6 .....................................................
Hypotheses 5 and 6 .....................................................
Hypothesis 5 and 6 .....................................................
Hypothesis 7 .................................................................
Hypothesis 7 .................................................................
Hypothesis 7 .................................................................
Hypothesis 7 .................................................................
Hypothesis 7 .................................................................
Hypothesis 7 .................................................................
Hypothesis 8b .....................................................

Hypothesis 8b .....................................................

103
104
105
106
107
108
109
110
111

Figure 1:
Km 2:

LIST OF FIGURES

The Conceptual Model .....................

Theoretical Interaction for Hypothesis 4

INTRODUCTION

Skilled behavior is essential to virtually all jobs, ﬁom typist to truck driver, x-
ray reading to radar operation. Training for most jobs therefore necessarily involves
some degree of skill acquisition. Although skill acquisition has been systematically
studied since the late 18005, until the 19705 this research was generally conducted on
simple, well deﬁned tasks concentrating on issues such as practice, feedback, and
interference effects (Baldwin & Ford, 1988). “With the increased prominence of
cognitive psychology, which places more focus on human learning processes, came
new interest and new theories regarding the learning of skilled behavior — particularly
the acquisition of more complex skills than had previously been studied

This interest in complex skill acquisition parallels changes in the workplace.
Great demands for effective skill training are imposed by increasingly complex jobs,
increasingly sophistieated technology, and the acknowledgmmt in business that
training is an important way for organizations to gain a competitive edge (Goldstein &
Gilliam, 1990; Proctor & Dutta, 1995; Rosow & lager, 1988). Organimtions and
researchers are searching for ways to increase the eﬁciency of training, allowing
employees to learn skills better and faster, in order to reduce training time, lost work
time, and training costs.

To this end, the cognitive psychology literature has produced theories and

programs which advance orn~ understanding of the processes involved in skill

1

2
acquisition (of. Anderson, 1987; Gagne & Glaser, 1987; Kanfer & Ackerman, 1989).

Much of the recent attention in the I/O literature has been on the limited resource
model of skill acquisition This model has advantages over other skill acquisition
dreadesbeewseitmtegratesmanyhnponantfaaorsﬁomﬂreUOﬁtaannesuchas
abilities, and motivation with newer concepts from the cognitive literature such as
cognitive resources and resource allocation

The pmpose ofthe present study is to examine and build upon the irnplieations
ofthe limited resource model. It is suggested that although research on the limited
resource model provides useful insight regarding factors that can inhibit the acquisition
of a complex skill, it is limited regarding how much help it provides to those wishing
to enhance skill acquisition The educational and instructional design literatures,
however, have identiﬁed factors that enhance learning. The proposed study focuses on
two potential interventions for the design and implementation of training: 1) the
sequencing of training through subgoals; and 2) enhancing metacognitive skills. It is
expected that these interventions help the learner focus their resources on activities
that mximize learning, and to make the best use of their available resources. A
conceptual model is proposed and hypotheses generated to clarify the relationships
between metacognition, learning goals, and performance attained by the end of

training.

 

 

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3
I 1.. I! l 5]."!

Starting with Ebbinghaus's (1885) early work on learning nonsense syllables,
the traditional approaches to skill acquisition have been to examine training design
factors which affect the learning, retention, and transfer of acquired skills. Training
design research has involved altering training conditions to examine the effects of
applying such learning principles as identical elements, general principles, stimulus
variability, and various conditions of practice.

As Baldwin and Ford (1988) note in their review, the application of these
principles has been found to increase trainee performance at the end of training for
some skills. Maintaining identical stimulus-response elements has been found to
increase performance on both verbal and motor skills (Gagne, Baker, & Foster, 1950;
Thorndike & Woodworth, 1901; Underwood, 1953). Teaching general rules and
theoretical principles underlying the skills being acquired has been shown to increase
performance on skills such as underwater shooting and eard sorting (Crannell, 1956;
Hendrickson & Schroeder, 1941; McGehee & Thayer, 1961). The inclusion of several
training stimuli to increase the trainee's tmderstanding of concepts and their
applicability in multiple situations has been found to increase performance on such
skills as lever movement and distinguishing spatial arrangements (Adams, 1957;
Dunean, 1958). Finally, many different conditions of practice have been studied as
training design factors. For example the distribution of practice, whole or part
practice, overlearning, and the provision of feedback have each received attention in

 

We

fﬁu

4
the literature. Naylor and Briggs (1962) and Briggs and Naylor (1963) provided

evidence that skills learned with distributed practice rather than practice massed at one
time are generally learned better and retained longer. Research also suggests that
trainingaskillwholeratherthaninpartsyieldsbetterperformance ifthe skill ishigh
in organization but low in complexity (Briggs & Waters, 1958; Briggs & Naylor,
1962). Teaching a skill past the point of successﬁrl performance, or overleaming, also
hasbeenfomrdtoincreaseperformanceon skillssuchaspairingwords, operatinga
control panel, operatinghandswitchestostimuli, andassernbly ofamachine gun
(Atwater, 1953; Gagne & Foster, 1949; Mandler, 1954; Schendel & Hagrnan, 1982).
Feedback, or providing information to trainees regarding their results, has also been
demonstrated as important to learning skills (Macpherson, Dees, & Grindley, 1948;
Thorndike, 1927; Trowbridge & Carson, 1932), and that speciﬁcity and timing are
critical in determining its effects (e. g. Wexley & Thornton, 1972).
Inadditiontoﬂieresemehonuainingdesignfactorstherehasalsobeen, more
recently, research to examine trainee characteristics affecting skill acquisition This
research generally involves pretesting the trainees to determine if the speed or success
of skill acquisition correlates with the characteristic of interest. The trainee
characteristies traditionally studied were abilities. Training samples as ability
measm'es have demonstrated moderate predictive value, for example Downs (1970)
found a training sample signiﬁcantly related to instructor scores for training on sewing
machine skills. Aptitude test batteries and ability tests have also predicted skill
acquisition to a moderate degree for record keeping skills (Taylor & Tajen, 1948) and

'l!

‘0.

 

5
supervisory skills (Neel & Dunn, 1960). Overall, however, the use of ability variables

to predict trainability has had only moderate success (Baldwin & Ford, 1988; Ghiselli,
1966).

The traditional research focusing on training design characteristics and abilities,
whileuseful, isalsolirrritedinscope. Inthetraditional approachesthetraineeis
treated as bringing his or her abilities to training to be acted upon by training designs.
Thesuccessofthesedesignswasprimarilymeasuredbysuccessonatestoftask
performance, orbyreducingthetrainingtimeto successful perfonnance. This
approach resulted in a stimulus-response orientation to training design possibilities
ratherthanaseries ofinterventionsbaseduponanlmderstandingoftheleamaandthe
learning processes, and the resultant learning outcomes. Perhaps due to the inﬂuence
of behavior-ism, the learning and knowledge acquisition processes remained largely
unexplained. For example, while it had long been observed that for many skills
performance progresses from slow and error-prone to rapid and accurate (i.e. since
Bryan and Harter, 1897), the traditional approaches offered no theories as to how or
why performance changes in this manner, and thus what interventions were appropriate
for different phases of leaming The learners' thoughts and mental processes went, for
the most part, ignored or unexplained The general learning principles were
disconnected since no theory of the human learning processes involved in skill
acquisition guided their development Better models or ﬁameworks were needed that
would look at the skill acquisition process itself, the mental processes involved in this
learning, and the resulting outcomes of knowledge acquisition processes beyond the

6
typieal better performance or faster acquisition. Conrprehension of the critical
psychological factors in the skill acquisition process was needed both to make better
use of the existing principles, and to improve our understanding of how to structure
the learning environment and prepare learners for skill acquisition.

Cognitive psychology has emerged to a large extent due to dissatisfaction with
behaviorists' stimulus-response approach because the S-R approach did not reveal
exactly what a person does with information that is presented in a stimulus. Cognitive
psychology takes an information processing approach in order to better understand the
processes and outcomes associated with acquiring knowledge (Reed, 1988). In effect,
ﬁlling in the black box between the stimulus and response - or, the training design
and resulting performance. Cognitive psychologists have tended to use stage theories
to examine the processes involved in moving ﬁom the laborious error-prone
perfonnarrceofthenovicetothesmooth, accrnateperforrrmcecharacterizingerqaerts
at a skill.

One inﬂuential framework for understanding skill acquisition and the different
natme ofperformance as it progresses was proposed by Fitts (1964; Fitts & Posrrer,
1967) who suggested that different cognitive processes are operating during different
stages of leaming He thus identiﬁed three phases of skill acquisition: the cognitive
phase, the associative phase, and the autonomous phase. The cognitive phase as the
name suggests, draws heavily upon the cognitive processes of the learner as he or she
isattemptingtomrderstandthenatmeofthetask andhow it shouldbeperformed
The cognitive processes taking place during this phase include attending to outside

 

01‘ :

iii.

ﬁr.

7
cues, instructions, and feedback Because of the heavy cognitive activity, performance

is slow and contains errors. Once the task instructions are learned and expectations
understood, the associative phase begins. During this phase, the learner links or
associates inputs more closely with actions. Verbal mediation decreases, there is less
interference from outside demands, and attentioml requirements decrease. During this
phase, errorratesandperformancetimesarereduced. Finally, whentheautonornous
phaseisreached, perforrnanceissaidtobeautornatic, nolongerrequiringconscious
control.

Anderson (1982; 1987) built upon Fitts's work in developing a theory of skill
acquisition that considers the speciﬁc processes and mechanisms underlying
performanceateachphaseofsldllacquisitionaswellasbetweenphases(Proctor&
Dutta, 1995). At the beginning of skill acquisition, a learner has little or no domain
speciﬁc knowledge. Therefore, all knowledge acquired in Anderson's Adaptive
Control of Thought (ACT) model starts out in a declarative form, that is, knowledge
of facts and information Declarative knowledge is often referred to as "knowledge of
what". Declarative knowledge can be encodings of examples of instructions,
encodings of general properties of objects, or other basic information gained through
experience, reading text, studying examples, or instruction To this declarative
knowledge, weak problem-solving rrrethods are applied. Weak methods are general
problem solving heuristics which can apply to a variety of domains. Examples of
weak mahods given by Anderson (1987) include means-ends analysis, working
backwards, hill climbing, and pure forward search The weak methods help the

8
learner to take initial steps in solving the problem, but performance is awkward and

error-prone. To advance, knowledge must move from declarative to procedural.
Procedmal knowledge is based on productions, which are essentially if-therr condition
action rules. Knowledge compilation is the crucial process through which knowledge
moves from the declarative to procedural form, producing a domain speciﬁc skill.
Compilation is comprised oftwo essential processes: 1) proceduralimtion, in which
necessary declarative knowledge is built into new domain speciﬁc productions; and, 2)
composition, in which several sequenced rules are collapsed into a single rule which
does the work ofthe sequence. As compilation takes place, and production rules are
created and collapsed, there is less need to keep declarative information in working
manory since the information is built into productions. Performance becomes
smoother, faster,andenorsarereduced Twofactors, strengthandworkingmemory
limitations, determine the success of production execution Strengdr determines how
rapidly a production applies, and accumulates when the production is applied
successfully. It is production strergthening that predicts the typical power-flmction
rate of progress in skill acquisition Working memory limitations refer to the idea that
evenperfectproductionsequenceseanfailifcritieal inforrnationislostduetotlre
limited eapacity of working memory causing the wrong production be used However,
Anderson(1987)notesthatworicingmemorycapacity increaseswithexpertiseina
domain Finally, with continued compilation, strengthening, and increased working
memory eapacity a skill reaches autonraticity, in which the skill no longer requires

conscious attention, and other tasks can be carried out simultaneously without

decreased performance.

Based on Anderson's work, the process of skill acquisition can be deﬁned as
the process of moving from declarative knowledge to procedural knowledge and
ﬁnally to automaticity. That is, acquiring a skill is acquiring facts and information
and then compiling this information to an if-then rule based form, and ﬁnally
strengthening rules until automaticity in which no conscious eﬂort is needed to
perform The use of this deﬁnition, and the work of Pitts and Anderson, clearly
dermnshﬁessldﬂacqﬁsiﬁmasaprocessMresemchasmenowsaiouslyuyingto
understand ﬁom a cognitive perspective. Researchers have identiﬁed a number of
factors which, ﬁ'om the cognitive perspective, may inﬂuence the skill acquisition
process. The following section will examine the research on some of these factors.

2 .. E It], .51.!“

C . . B .
One of the main factors inﬂuencing skill acquisition identiﬁed by cognitive
psychologists is cognitive resources. Cognitive resources are mental capacities such as
attention, (Kalmernan, 1973; Kanfer and Ackerman, 1989), processing eﬁ‘ort (Norman
& Bobrow, 1975) or various forms of memory (e. g working memory, Stankov, 1983)
whicheanbeallowedtotasksinordertobringabomperfonnance. Asindieated
earlier, Pitts and his associates noticed that as one acquired skills, the instructions and

expectations become encoded and no longer require as much attention The

10
diﬁ'erential alloeation of cognitive resources in terms of direction and amount of

attention, therefore, beeame an important area of research on skill acquisition This is
especially true as theories of attention themselves evolved

Kahneman (1973) was an important contributor to cognitive resource theory.
Prior to Kalmeman’s (1973) limited capacity theories, most of the research that had
been conducted on attention had bear focused on the bottleneck models which
postulated that information processing capacity is limited at a certain point in the
processing, a bottleneck of sorts. Unfortlmately, various authors (among them
Broadberrt, Treisman, Deutch and Deutch, and Norman) and their research results
disagreed as to the loeation of such a bottleneck. With his limited capacity model,
Kahneman was able to shift interest from the bottleneck theories to the capacity
demands oftasks. Capacity theories assume that there is a limit on human capacity to
perform mental work, and this capacity is alloeated with considerable ﬁeedom
(Kahneman, 1973). According to Kalmeman's rrrodel, a number of possible activities
receive information input and we select which activities to pursue by adding mental
mom in the form ofmental effort, or attention to this input. Different tasks
require different amounts of attention, simple tasks need little, difﬁcult tasks require
more. Whenthesupply ofatterrtiondoesnotmeetdernands, performance falters, or
fails. In effect, Kalmernan states "we merely decide what airm we wish to achieve.
The activities in which we then engage determine the effort we exert" (p. 14).
According to the model, an allocation policy is responsible for dividing up the pool of

attentional effort resources among the various demands. An "evaluation of demands

11
on capacity" acts as a feedback mechanism to the allocation policy and

arousal/capacity level. "Interference between tasks is due to the insufﬁcient response
of the system to demands, and to the narrowing of attention when effort is high" (p.
16). This model demonstrates both the intensive and selective nature of attention in
that it shows both amount and allocation of effort It is capacity that dictates whether
or not tasks can be performed in parallel without detriment to either. This view, that
human attention exists as a common "pool" that is divided among tasks which
themselves have differential requirements for successful performance is directly
reﬂected in current skill acquisition models.

Norrmn and Bobrow (1975) modeled the normative eﬂ'ort- onnance
relationship in skill acquisition implied by Kahnermn developing their Performance-
Resource Function (PRF). Norman and Bobrow introduce two important categories of
tasks: resource-limited and data-limited. When increasing the resources devoted to a
taskwillyield irnprovedperformancethetaskis saidtoberesourcelimited
Decreases inresources(say, ﬁomdhddingresomcesbetweerrtasks) neednoteause
failure, simply decreased performance.

Whenever performance is independent of processing resources, the task is data-
limited. Some tasks are so simple that the processing performance is limited by the
simplicity of the data structure. By way of example, Norman and Bobrow consider a
personatterrlptingtohearthepiano inaroomfull ofothernoise; onceallthateanbe
donewﬁhe'ombackglomdsomdshasbemauanptedperfonnmceisbasedsuialy
ontlrequalityofthedata. Increasedallocationofresomcescanhavenoﬁrrtlrereﬂect

12
on perfonnance, thus the task is data-limited Norman and Bobrow indicate that most

tasks will be resource limited up lmtil the point where all the processing that can be
done has been done, and data limited from that point on From these concepts,
Norman and Bobrow constructed a function relating perfonnance to resource allocation
placing resources along the x-axis, and performance along the y-axis. The flmction is a
monotonically non-decreasing function; generally difﬁcult task ﬁmctions are thought of
as s-shapcd Moving along the x-axis, initially there is little improvemert due to
insuﬁcientresources, butastheresourcessmpassthethresholdneededtopromote
mininml leaning and performance, the ﬁrnction becomes steeper, leveling oﬂ' at the
upperlimitofprocessing Atthepointwheretheﬁrnctionreachesanasymptotein
perfonnance,thetaskisdata-limited. Leaningisdemonstratedbyfastersteeper
climbs to the asymptote, leading to successively more coneave curves. That is, less
andlessresomcesaerequiredformaximmnperfonnanceasoneleans. ThePRF is
quite consistent with Fitts's model, corresponding with the decrease in required
resources as leaners encode the instructions and expectations as they move through

the cognitive and associative phases to the autonomous phase. Less attention is

requiredforbetterperfonnance.

C . . El '1' . .
Researchers of skill acquisition have also continued to investigate the

relationships between abilities and performance. Reseach has demonstrated that

cognitive abilities play an irrrportarrt role early in the skill acquisition process, with

13

other abilities (perceptual and motor) becoming more important later in skill
acquisition Woodrow (1938) was among the ﬁrst to examine the whether or not
abilities changed over the course of skill acquisition Using factor analysis Woodrow
noticed that correlations of performance with intelligence tests decreased as skill
acquisition progressed Fleishman and his associates continued this reseaeh yeas later
(Fleishman & Hempel, 1954; 1955). Fleishman and Hernpel (1954) gave subjects a
reference battery of ability tests and then trained them on a factorially complex motor
criterion task. Criterion scores and task scores were intercorrelated and factor
analyzed. The results showed that sevaal cognitive variables were important early in
skill acquisition, but later in acquisition motor factors and task speciﬁc factors
dominated. Theauthors foundthattheirresults generalizedtoadiscrimination and
reaction time test (F leishman & Hempel, 1955) with cognitive abilities like spatial
relations and va'bal factors accounting for variance eaiy in acquisition, and motor
abilities like reaction time and rate of movement accounting for variance late in
acquisition

Thisworkhasbeeneariedﬁntherardappliedtocognitivetasksinrecent
research by Ackerman. The beginnings ofhis current model date back to earlia'
versions Ackerman (1986; 1987). In this work, Ackerman brings several of the
theories regarding cognitive factors affecting skill acquisition together. Particularly,
Ackerman integrates work ﬁom Norman and Bobrow's (1975) theory regarding the
PRF, and Shifﬁin and Schneider's (1977) controlled vs. automatic processing

14
At ﬁrst novel or difﬁcult tasks are what Norman and Bobrow eall resource-

lirnited, more resource yield better performance. However, as Shifﬁin and Schneider
demonstrated, tasks are inconsistent no automatic processing will evolve ever with
consideable practice and pe'fonnance will rennin resource-deperdert. On the other
hand, ifataskhasthe consistent characteristics necessarytosupportthe development
of automatic processing, the task will become less resource-dependent and more data-
limited As a task becomes totally automatic tlrer fast effortless peformance is a
possibility.

Ackerman maps his ﬁrsing of the PRF to controlled-automatic processing onto
the abilities debate, noting that Kahneman's (1973) theory of lmdiffeentiated structure
of attention is roughly equatable with Spearman's geneal intelligence factor.
Ackerman (1986; 1987) develops three important principles: 1) individual diﬂ'ererces
in broad geneal ability are equatable with individual differences in amount or
eﬁciency of attentional resources; 2) the transition ﬁom controlled to automatic
processing is equatable with resource-deperdert to resource-insensitive pe'formance
characteristics; and 3) the ability deteminarrts of performance are associated with the
extentandtypeofresourcesrequiredbythetask Theseprinciples leadtoamodel of
skill acquisition which reﬂects Fleishman and Hernpel's (1954; 1955) ﬁndings that
cognitive abilities dominate early in skill acquisition and psychomotor abilities
dominate later. In Ackerman's model, initial performance on any task is highly
resource-dependent and thus performance is determined by g, and general content
abilities (spatial, verbal, etc.). For tasks with inconsistent characteistics, gereral and

15
content abilities continue to be the key to pefonrrarrce throughout as the task remains

resource-limited. Howeve, as consistert tasks become data-limited, automatic
processing develops and only the speciﬁc skills/processes that overlap autonmtic
processing become predominant factors for perfonnance; the task becomes ability
insersitive ﬁom the standpoint of g and contert abilities. Ackerman (1986; 1987)
tested this model, measuring various types of meltal abilities including g, corltert
abilities like spatial and vebal, as well as abilities believed to detemine performance
laterinthetaskwher it isresource inselsitivesuchaspsychomotorandpe‘ceptual
speed. Ackeman assessed skill developmert on both vebal and spatial tasks with
consistert and inconsistent mapping similar to those used by Sclmieder and Shiﬂiin
(1977). Ackeman correlated pe'formance with the various abilities and the resulting
performance-ability ﬁmctions were equatable with the PRF, and consistert with his
theory. Overall, the ﬁt of the PRF to the progression of abilities seemed promising
though in need of reﬁnemert.
ThisreﬁnemertoccurredinAckerman(l988)inwhichthemodel isalteredin
twoways. First, itsstructureisadaptedtomakeitradex—basedandhiearchieal in
nature. Second, Ackemans's (1988) model is integrated with the three phases of skill
acquisition suggested by Fitts and Posner (1967) and Anderson (1982; 1987). On top
of the model is g (gereral cognitive ability). Just below g are the broad content
abilities (ﬁgural, numeical, and ve'bal - which is most important depends on the
corrtert of the task). Gereral cognitive ability and the content abilities are said to be

important for the ﬁrst stages of skill acquisition - procedural knowledge. As one

l6
begins the second stage of skill acquisition, knowledge compilation, peceptual speed
becomesmoreimportanttoperfomlance. Finallyasthethirdphaseofprocedural
knowledge begins and automatization occurs, psychomotor ability emerges as the
predominant determinant of pe'fonnance.

Ackeman (1988) presents eight experimental manipulations beginning with
reaction time (RT) choice discrimination tasks like those used in previous skill
acquisition study, but erding with a more complex air traﬂic controller task. The
experimerts manipulated the relevant task variables such as consistency and
complexity and examined correlations with perceptual speed and general ability
through skill developmert Furthe work on a task with more inconsistert componerts
was also performed in Ackerman (1992). The results of these erqreriments clearly
indicated overall support for the model. For consistent tasks g and content abilities
are the major deteminarrt of performance early, but as acquisition progresses
perceptual speed and then psychomotor ability become more important. For
inconsistent tasks, g and geneal content abilities are strong and consistert

determinants of performance.

I l . .
In addition to resources and abilities, psychologists have also studied how
personal cogrritions inﬂuence the acquisition of skills. IVhlch of the work in this area
involves cognitions petaining to motivation before and during skill acquisition Two
of the most thoroughly studied of these variables are self-efﬁcacy and self-regulation

17
W. Self-efﬁcacy has been deﬁned as an individual's belief in his or her

capability to perform a speciﬁc task, or task speciﬁc conﬁderce (Bandura, 1982).
Individuals obtain infonnation with which to make an efﬁcacy judgrnert is a number a
ways, such as performance accomplishmerts, vicariously through observation,
persuasion, and physiological indexes (Schunk, 1989). Information gathered ﬁom
these sources is weighed and combined in a cognitive appraisal taking into account the
peson and situational factors to make an eﬁicacy judgmert (e. g. peceived ability,
task diﬁiculty, amount of effort expenditure, assistance received, patterns of success or
failure, or pe‘ceived similarity to models). Bandura (1982) hypothesized that
perceived self-eﬁicacy aﬁ‘ects motivation by affecting one's choice of activities, effort
expenditure, and persistence.

Although early self-eﬁicacy reseach by Bandura and his associates was in a
theapeutic context (training people to cope with feaed situations), self—eﬁicacy has
beerexaminedasimportanttoleaningsldlledbehavior. Atthestartofaleaning
activity, leaners may diﬁ‘e' in their peoeptions of their capabilities to acquire
knowledge, perform skills, master the rnateial, and so forth. This affects the effort
they extend and ultimately skilled performance. Schunk (1981; 1982) taught a
cognitive skill to low achieving children either via a model or through step by step
instructional pages. Half of the subjects were giver effort feedback to erhance self-
eﬂicacy (they were told they solved problems because they worked hard, or got them
wrong because they did not put forth enough effort). However, this effort feedback
did not always seem to have the interded effects. While telling subjects that the

18
reason for their success is eﬁbrt did support self-efﬁcacy, telling them they did not

work hard enough when they got a problem wrong created the impression that they
wee failing, and thus that they wee not capable. These effects wee disentangled in
Schunk (1982) whee the hypothesized relation between self—efﬁcacy and performance
was examined by deriving the probability of performance as a function of eﬁicacy.
Schunk found that regardless of the treatment, higher eﬁieacy was found to be
associated with progressively greater skill. Path analysis indicated direct paths ﬁom
self-eﬁicacy to skill and pesistelce.

In fact, self-eﬂicacy has been found to predict performance in a number of
diﬁerent cognitive tasks, such as learning ﬁom print (Salomon, 1984), writing (Meie',
McCarthy, & Schmeck, 1984), and leaning mathematics skills (Relich, Debus, &
Walker, 1986). However, it is not just for cognitive tasks that self-eﬂicacy is found to
inﬂuerce perfonnance, motor skills are also inﬂuerced by self-efﬁcacy. F eltz (1982)
found that self-efﬁcacy and prior performance predicted subjects' ability to lean a
backdive, arrdthatpastperfonnalrceaﬁectedself-eﬂieacywhichimpactedﬁrtme
performance. Barling and Abel (1983) found a positive relationship between self-
eﬂieacy and aspects of temis perfonnance. Similarly, McAuley (1985) fornrd eviderce
for a positive relationship between efﬁeacy and performance in gymnastics. Eyring,
Johnson, and Francis (1993) found that cognitive ability, task familiarity, and self-
eﬂieacy predicted individuals leaning rates on the Air Trafﬁc Control (ATC) task, and
only self-efﬁcacy predicted asymptotic (ﬁnal) performance. Finally, Bandura and
Cervone (1983) found that self-efﬁcacy is important for goal attainmert. These

l9
resea'chers tested subjects given improvemert goals, feedback, or both in an e'gometer

task. Only the goals + feedback condition was effective in increasing effort. In the
goals + feedback condition, peceived self-efﬁcacy for goal attainmert signiﬁcantly
predicted subsequert effort among the subjects.

Clea'ly, self-efﬁcacy has substantial motivational impact on leaning and
performance on a variety of different skill types through its effects on the effort that is
put forth by the leamer.

SelﬁRemlatim Self-regulatory processes are related to self-efﬁcacy processes. Self-
regulation ought to affect skill acquisition by aiding people to reach their goals.
According to Karoly (1993) self-regulation refes to those processes that enable an
individualtoguidehisorhergoaldirectedbehaviorove‘timeandacrosscontexts.
Regulation implies thought, affect, behavior, or attention via the use of speciﬁc
mechanisms and supportive metaskills. Locke et al. (1981) proposed that goals
inﬂuence behavior by directing attertiorr, mobilizing on-task effort, encouraging task
persistence, and facilitating strategy developmert. However, most of the resea'ch on
goal setting has focused arolmd issues ofhow various aspects ofthe goal impact
perfonnance. Diﬁicult, speciﬁc goals have beer demonstrated to be effective on all
but ve'y complex tasks. Self-regulation theories of motivation focus more precisely
on how goals help the individual to mobilize effort, maintain persistence, and direct
attention According to Kanfer (1992), self-regulation theories generally come from
three perspectives: social leaning, cybenetic control, and resource allocation

Social leaning theories view goals as giving the individual a cognitive

20
representation of desired outcomes (Bandura, 1986). Self-regulation toward these

goals involves self-obse'vation, self-evaluation, and self-reaction (Bandura, 1982; F.
Kanfer, 1970). Self-observation refes to directing attention toward one's own
behavior. Self—evaluation involves comparing this behavior to the goal. Wheeas, self-
reactions are affective reactions and efﬁcacy outcomes for future goals. If the self-
evaluationrevealsthatthe individual isfar'ﬁ'omreachingtlreirgoaLthismayresultin
a self-reaction of dissatisfaction and, assuming the person feels they are capable of
reaching the goal, increased effort If the peson feels they are not capable of reaching
the goal (low self-efﬁcacy), then the discrepancy between current state and goal state
rmy have no impact.

Cybenetic control theories of self-regulation date back to Carve and Scheir
(1981) building upon the work of Powes (1973). Their theory is based on the
negative feedback loop and a hierarchical goal structure. In control theories, the goal
iscomparedtoﬂrecmrentstateandwhenthereisadiscrepancy, cognitiveand
behavioralomputareeractedtowardreducingthediscrepancy. Whertheeis
diﬂicultyreducingthe discrepancy, then attentiondropstoalowe‘ordergoal inthe
hierarchy. When this lower goal is reached, ther attertion can shift back to the higher
order goal.

Finally , resource allocation theory of self-regulation posited by Naylor and
Ilger (1984) is a cognitive ﬁarnework attempting to explain choices among acts as
competing options for resource allocation The individual attempts to maximize their

anticipated inﬂuence under a set of constraints. Motivation is viewed as involving

21

expected utility consideations in that the individual weighs their affect against self
peceptions of the behavior-outcome contingercies. This theory holds that there are
three basic contingency relationships in motivation: commitment to the act is related
totheproductproducedbytheact(A-P),theproductoftheactisrelatedtothe
evaluation of the product by and observe (P-E), and the evaluation of the product is
related to outcomes resulting ﬁom the evaluation (E-O). Setting a goal for someone
in terms of output thus affects their P-E relationship. The goal in effect changes the
zero point (minimally acceptable output for a neutral evaluation) of the P-E
relationship thus changing the balance of the other relationships. This theory oﬂ‘es an
explanation for why ovely difﬁcult or non-speciﬁc goals may lowe performance by
alteing the perceived utility of various allocations of effort

In sum, the reseach on the cognitive variables that inﬂuerce skill acquisition
yielded some progress, but this progress been made in different directions. Some
research has progressed our lmderstanding and theorizing about resource allocation,
othe research focused on the changing nature of abilities during skill acquisition, and
still othe research addressed motivational directions involving how and why we
expend effort. Thus, each of the models could explain some aspects of skill
acquisition, but the inﬂuerces of abilities and the inﬂuerce of motivational processes
renained separate. Both the abilities resea'ch and the motivation research dealt to
some extert with the nature ofcognitive resources and how they are applied, but a
theory was needed to bring the two together. In effect, to combine the reseach on
abilities with the research on motivation as they reﬂected the newe cognitive concepts

22
of skill acquisition Such a model was set forth in a monograph by Kanfe and

Ackeman (1989).

In their monograph, the authors pose the limited resources model of skill
acquisition, an integrative aptitude by treatment interaction model for the ways in
which ability and motivation affect performance. The authors posit attertion as a core
construct, citing Norman and Bobrow's resomce-limited/data-limited distinction and
PRF. In aldition, they draw on Andeson's (1982) skill acquisition model. Kanfe and
Ackeman note that as leanes move ﬁom declarative knowledge to procedmal
knowledge and automaticity, pefonnance requires less atteltion and thus the task
moves ﬁom resource-limited to data-limited The processing of declarative data is
resomce-depeldent eaiy in skill acquisition since great demands are placed on
cognitive resources. As the leane progresses through compilation and
proceduralimtion, howeve, the skill becomes automatized and less resources are
required ﬁeeing resource for othe activities. As with ea'lie' Ackeman reseach, the
abilities crucial to perforrmnce are posited to be gereal cognitive ability and broad
content abilities ea'ly in skill acquisition, but progress to perceptual and psychomotor
ability as the skill is proceduralized In Kanfe and Ackerman (1989), thee is an
equating of gereral ability with cognitive resources such that those with more geneal
cognitive ability are viewed as having more resources to devote to the task at the start.

The major contribution of this limited resources model of skill acquisition to
the literature involves the inclusion of motivational components in the model which

are quintessentially the allocation policies proposed by Kahneman's "evaluations of

23
demands on eapacity". Motivational processes in the Kanfe and Ackeman model

are separated into distal motivational processes and proximal motivational processes.
Distal processes involve the choice to ergage some or all of one's resources for
attaimnert of a goal and involve three utility relations espoused in Kanfe (1987).
Like the Naylor and 1] gen model, resource allocation decisions are made based upon
perceptions of utility contingercies. In the limited resources model, there are three
fundamertal relationships: effort to performance (E-P), performance to utility (P-U),
and eﬂ°ort to utility (B-U). Resource allocation is a main deteminant of the effort to
performance relation Norman and Bobrow's PRF is esseltially an EP relationship
based upon task characteistics. However, the E-P relations are viewed as having only
indirect inﬂuerce on pefonnance through their function of linking utilities to goals
andactions. Distalmotivationprocessesareantecederttotaskergagemert, arrddo
not draw resources ﬁom the leaning task. Wher goals involve complex or novel
skills, goal attaimnert necessitates sustained attentional effort in orde to ovecome
difﬁculties and requires "iteative, proximal motivational activities" to sustain the
attentional effort (Kanfer & Ackeman, 1989, p.661).

Proximal motivational activities are responsible for the distribution of
attertional effort to on and off task activities while performing the task. According to
the limited resources model, the proximal motivational activities togethe comprise
self-regulation These activities diﬁe ﬁ'orn distal processes because: 1) they are
enacted while engaged in the task; and 2) require the devotion of critical attentional
resources, potertially taking resources away ﬁom task performance. Wher thee are

24
little slack resources (i.e. peiods of high engagenert) the result may be atteruated

performance. It should be noted that the flmction of self-regulation in the limited
resources model is to detect changes in the perfonnance-resource ﬁmction as skills are
autonmtized and allow for the reallocation of the resom'ces as necessary. If the results
of this reallocation are bereﬁcial to goal attainmert, ther the additional resources can
ultimately outweigh the costs causing an increase in performance.

Self-regulation in the Kanfe and Ackerman model reﬂects Social Leaning
models described earlie in that self-regulation is comprised of three activities: self-
monitoring, self-evaluation, and self-reaction Self-monitoring refes to the focusing of
attention on speciﬁc aspects of pefonmnce or behavior, and the consequerces of the
behavior. Self-monitoring is not necessarily accurate, and erors in judgemert about
one's capabilities can lead to insuﬁiciert allocations of attertion, and thus, insufﬁcient
performance. Self-evaluation, the authors note brieﬂy, is the corrrparisorr betweer
deshedgoalstateandementperfonnance. Thesizeanddirectionofanydiscrepancy
is postulated to irrteact with self-reactions to inﬂuerce late decisions about allocation
of resornces.

Finally, self-reactionseanbeoftwotypesinthemodel. Theﬁrsttypeis
aﬂ‘ective ealled self-satisfaction and the second involves perceptions of task-speciﬁc
eapability or self-eﬂieacy. One's self-satisfaction and self-efﬁeacy can be positively or
negatively inﬂuerced depelding on the size and direction of the discrepancy betweer
currert and desired state examined in self-evaluation.

Self-reaction, and its interaction with self-evaluation, have important

25
implications for the application of resources, and for the importance of goals. Because
tasks require less resources as they are procelmalized, goals keep people motivated by
setting diﬂicult performance standards against which to self-evaluate yielding more
dis-satisfaction (due to a presumably larger gap betweer desired state and currert
state). As long as thee is sufﬁcient efﬁcacy, the results are continued self-regulation
and resources application When goals are reached, self-satisfaction occurs, and self-
regulatory W stop-

Kanfe' and Ackeman posit an inteaction betweer goal (motivation) and
ability such that those of high ability can make use of goals earlie, but those of low
ability bereﬁt nrore ﬁom using goals. Initially on a novel complex task, leanes
must use all of their resources to lean the task Self-regulation early is postulated to
take away ﬁom the resomces devoted to leaming which attenuates perfonnance.
Because high ability people have more resources to apply, they will lean faste and
more quickly reach a point at which they have sufﬁcient resources ﬁee for self-
regulation Low ability people, howeve, will beneﬁt more ﬁ'om a propely applied
goal, since they start lowe and have more room for improvemelt.

Threeertpeimentswee conductedtotestthemodelusingthe complexAir
Traﬁic Control (ATC) task Results indicated that goal setting did not improve
performance wher goals wee set early in skill acquisition. The patten of correlations
for abilities was as they predicted The expected inteaction between ability and goal
intervention was found such that those with highe ability bereﬁtted earlier from
having a goal. Declarative training loweed initial cognitive load allowing goal setting

26
to be effective earlie, but procedural training did not allow the goal setting

irrtevention to be effective earlie. Low ability people in the declarative training

group bereﬁtted the most over time.

The skill acquisition theory and reseaeh to date, exempliﬁed by Kanfe and
Ackeman's (1989) limited resources model, has led to a deepe undestanding of the
cognitive processes involved in skill acquisition A nurnbe of important ﬁnding have
emeged One ﬁnding is that limitations on human cognitive resources play an
irnportarrtpartinskill acquisition Thereseachhasdenonstratedthatea'lyinsldll
acquisition of a complex novel task with consistert elements, the leane is taxed in
thatheorshemustatternpttoapply lirnitedresourcesto leaningthedeclarative
knowledge. These processes of encoding the instructions, expectations, and task
information are resource intensive, and preclude the leane ﬁorn successﬁrlly
peforming othe activities, like self-regulation, without atteruating the peformance of
both. Thefactthathmnansarelimitedinresomcecapacity, andthatpeoplediffein
the amount ofresources they have to apply can account for why it takes longe to
acquire difﬁcult tasks, why people cannot perform multiple novel tasks with success,
andtosomeextertwhypeoplediﬁ‘einthespeedwithwhichtl'leyacquiretasks.

A second important ﬁnding is ﬂat tasks are not equally resource consumptive
throughout acquisition Virtually all cm'rent theory recognizes this fact, most clearly
modeled by the PRF (Norman & Bobrow, 1975). As a novel task is compiled, the

27
declarative information becomes linked to task behavior decreasing the need to keep

this knowledge active in memory. With practice the production rules develop, and the
task becomes less resource dependert until automaticity in which the task become
data—limited Kanfe and Ackeman (1989) demonstrated that subjects pefonning their
ATC task wee not able to regulate to performance goals early in skill acquisition and
that attempts to do so actually wee detrimental to peforrnance. Howeve, whel
comparable goals wee provided afte declarative knowledge was leaned, enough
resources had been ﬁeed to make self-regulation possible without taking resources
ﬁomleaning. Theeldresultwasthatpe'formance increased Thus, inplanrringa
useful intervention thee is a need to consider whee and how resources are being
used

Athird important ﬁnding ﬁomthemorerecentskill acquisitionworkmakes it
clear that self-regulatory processes play an important role in the acquisition of a
complex skill. Since the task itself essentially detemines how much resources are
requiredforleaming,theonusisonthelearnetomakesmethatsuﬂiciertresomces
are devoted to the task to fulﬁll these requirements. Insuﬂiciert resources may lead to
detriments to leaning and performance. How well a peson is able to determine the
taskresomcedenandsandaﬂocateﬂreappropﬁateresomceswillﬁrmaaﬂleir
performance when acquiring difﬁcult tasks which require iteative allocations of
attertion This is true particularly if leaning different task elemerts requires diﬁ‘eent
quantities of resources. i

While the skill acquisition reseach has identiﬁed cognitive resources and

28

regulation as important variables in the processes of skill acquisition, thee are seveal
limitations ﬁomatrainingperspective. The ﬁrstproblem isthatthis reseach is
descriptive, not prescriptive. Simply put, the research tells us what occurs during
learning, but has not yet indicated inteventions traines may use to help leanes to
acquire skills. In fact, the Kanfe and Ackeman (1989) work denonstrates what not
to do — give leaners pefonnance goals early in training. What interventions trainers
eanusetoaidleanesinmaldnguseoftheirresomcesremainstobeclariﬁedand
demonstrated by systematic research.

Asecondshortcominginthisreseachisalimitedviewofpefonnance. Whel
a peson is acquiring a skill, they are essertially balancing between leaning the skill
and performing the skill. The two are not necessarily mutually beneﬁcial. Sometimes
leaning involves exploration, making mistakes on purpose, or other behaviors that will
not lead to good performance. From atraining point ofview, leaning itselfis atype
of "peforrnance" in the acquisition of a skill. Therefore, it is wholly appropriate to
setgoalsnotsimplyintemsofpeformanceintheoutcomeserse(i.e. nurnbeof
planes landed, points scored etc), but also in terns of leaning. This is particularly
true early in skill acquisition while the peson is devoting the majority of cognitive
resources to the ercoding of declarative knowledge, and compiling this information to
producﬁons. Kanfe and Ackeman, howeve, only gave their subjects outcome
peforrnarrce goals, causing then to split resources betweer leaning the task,
performing, and self-regulating. This is well demonstrated in the third study in Kanfer
and Ackeman (1989). Expecting high performance early in the skill acquisition

29
process is fairly unrealistic to most training situations. In training, leaming is the
shorttenngoal, and isexpectedtoresultinhighperformanceinthelongrun. Thus,
a failure to recognize leaning as a form of perfonnance during skill acquisition and
consequertly the use of only outcome-based performance goals is a second problen
with the skill acquisition liteature to date.

A third problen with is the faillne to examine how the devotion ofresources
to a problem produces better performance. The limited resources model, like othe
rrrodels, stops at the distinction between "on-task resources" and "off task-resources".
To sirrrply say that more on-task resources will be added and pefonnance will
theefore irnproveis incomplete. Theseresourcesneedtobedevotedtosome
activities which result in leaning, pe'fonnance, or self-regulation. Similarly, the
limited resources model do not cove strategy evaluation as part of self-regulatory
behavior. Thisneglectswhat is actually donewiththeresomcesdevotedtothetask
As itiswithpoliticians, themodelsseemtosuggestﬂratsimplythrowingresomcesat
aproblemiseroughtosolveit. Ofcomsethewayinwhichtheseresourcesareused,
the activities and strategies in which the leane ergages, produce more or less
leaming and performance. So, we must make an effort to undestand what "on-task"
activities are (lining skill acquisition, and to detemine how resources are allocated on
the task, and impact skill acquisition

30

!I .. E .
From a training perspective, a certral issue is how the limited resource model

ear be used to help improve skill acquisition Research in this area is decidedly
lacking The limited resource model is an integrative attempt to undestand the
processes of skill acquisition The model is meant to bring togethe various theories
of skill acquisition, and to clarify the cognitive processes involved Howeve, while
thee has been substantial support for this model or its corrrponents (Ackeman, 1988;
Ackeman, 1992; Kanfer & Ackeman, 1989; Kanfe et al., 1994) the implications of
thismodel fortraininghavenotbeeridentiﬁedandtested Whilethecurrertreseach
is informative regarding what not to do (i.e. set pefonnance goals ea'ly in
acquisition), extensions to what should be done to inrprove skill acquisition are
needed Idertifying and testing the types of interventions that have implications for
the use ofthe leane’s cognitive resources is theefore an important next step toward
increasing the efﬁcacy of the limited resource pespective of skill acquisition

Two such intevertions have beer choser for examination in the currert study.
The ﬁrst intevertion is the sequencing of instruction by setting systematically
sequerced subgoals. The second intevention is metacogrritive training prior to skill
acquisition These intevertions wee chosen because they met three essmtial criteia:
1) thee are solid theoretical reasons grounded in the limited resources model of skill
acquisition to expect these intevertions to elable leanes to improve skill acquisition
by making bette use oftheir resources; 2) these intevertions have beer found to be
effective in othe literatures (i.e. the instructional design and educational liteature);

 

31
and 3) these intevertions are ones that traines can tailor to virtually any skill
training. While thee are undoubtedly othe intevertions meeting these criteria, the
aforementioned intevertions appear to be at least a logical initial effort to build upon
the limited resource model.

The skill acquisition literature has ignored the sequerce of training as having
impact on leaning. The skill acquisition liteature suggests that for tasks with
consistert elemerts, skill acquisition progresses through stages (Andeson, 1982;
Andeson, 1987; Fitts & Posne, 1963). Theefore the sequerce in which one leans,
andthegoalsrelevarrttothisleaning, oughttobearrangedaccordingly. Oneway
whichhasbeenfomrdeffectivetodirectleaningistheuseofgoals, ormore
speciﬁcally subgoals, to direct the leane. Locke, et al. (1981) in their review state
that goals operate, in part, by mobilizing and directing effort. People behave in a way
consistertwithagoal to whichtheyarecornrnitted Forthisreason, learning subgoals
areaneﬁ‘ectiveway fortrainestodirectthe leaneandsequercetheirtraining
Rothkopf and Billington (1979) demonstrated that leaming goals seved effectively to
direct effort Likewise, Hoﬁnann (1993) suggests that leaning goals help the leane
to avoid detrimertal cognitive intefeence, such as that which occurs when the leane
is faced with nurneous corrrpeting denands. Thus, leaning goals are a useful way to
help learnes to sequence their leaning.

From a limited resources pespective, thee are clea' indications that subgoals
must be chosen carefully. As mertioned, Kanfe and Ackeman (1989) set

32
performance subgoals early and found them to be detrimental to leaming and

performance. This denonstrates that just having subgoals will not necessarily be
beneﬁcial, itisimportarttochooseﬂlesegoalssoﬂratﬂreyareconsistertudﬂrwhat
we know about the process of leaning and skill acquisition The subgoals must be in
anordetlmtallows the attaimnent ofearliesubgoals toﬁeeupresomces for late
subgoals. The subgoals should thus be based upon leaning theory, not choser at
random.

The Instructional Design literature explicitly addresses issues of instructional
sequelcing Thee are, in fact, seveal instructional sequencing theories (e. g Skimre,
1953; Issuable, 1960; Gagne, et al., 1992; Reigeluth & Stein, 1983). The one choser
for guidance hee is Gagne's hiearchical sequencing theory. This theory was chosen
beeause: a) it is one of the most well known sequelcing theories; b) this theory can
assist in the choice of logical subgoals, and c) Gagne's theory can explain why
knowledge gained in ea'lie subgoals aids in the leaning ﬁom late subgoals.

Gagne, et al. (1992) posit ﬁve types of leaning outcomes: vebal information,
intellectual skills, cognitive strategies, motor skills, and attitudes. Vebal information is
sirrrply what we commonly refe to as declarative knowledge of facts and infonnation
Intellectual skills are the "how to" types of knowledge associated with what has been
called procedural knowledge. Cognitive strategies goven one's own leaning,
rernernbeing, and thinking; they lurve beer referred to as self-regulation and self-
monitoring, and include comprehersion monitoring and other metacognitive activities.

Motor skills are leaned capabilities that undelie peformances whose outcomes are

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33
reﬂected in the speed, accuracy, smoothness, or force of bodily movemert. The

performance of motor skills embodies intellectual skills in the guiding procedures and
executive subroutines . Finally, attitudes are affective, pesisting states that modify the
individual's choices of action A positive attitude toward something means a person
will terd to do that thing more often. As this pape is focusing on cognitive variables,
attitudes as affective componerts will not be a main concem. Likewise, since motor
behavior embodies intellectual skills, consistert with previous discussion, the focus is
on the intellectual skills responsible for complex motor behavior.

Gage and his colleagues believe that goals in leaning exist in a hiearchy
with lowe goals containing the prerequisites to the accomplishmert of late goals.
GageandMerill (1990) suggestthatleameneedstoacquireagoalschenaealyon
in the leaning process. Gage, et al. (1992) write that "the schema relates the goal to
its preequisites, that is, to the kinds of skills and knowledge that the leane must
retrieve and use wher engaged in the enteprise" (p. 180). The lowe orde goals in
this hiearchy are refened to as "enabling objectives" and are eithe essertial or
supportive preequisites for speciﬁc pefonnance objectives. Gage et al. (1992) write
"the leaning of intellectual skills is most clea'ly inﬂuerced by the retrieval of other
intellectual skills that are prerequisite. Usually these are simple skills and concepts
that,wheranalyzed, arerevealedtobecomponents oftheskilltobenewly leaned
(RMGage, 1985). Results of analysis of this sort may be expressed as a leaning
hiearchy" (p. 111). Gage and his colleagues state that for intellectual skills, the most
direct effect of prior learning is through the retrieval of othe intellectual skills and

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34
information which are preequisite components of the current leaning. This notion of
hiearchieal goals has more recently beer adopted by authors more familiar to the I/O
liteature, most notably by control theorists such as Carver and Scheir (1981) as well
as Lord and Levy (1994). White (1974) also argues that the retrieval ofpreequisite
skills has direct supporting effect on leaning the targeted intellectual skill. Theefore,
it is important to make sure that the lowe portions ofthe leaning hiearchy are
attained ﬁrst to support and enable the learning of the highe orde skills.

Gage proposes that learners start with verbal information and simple
intellectual skills, and precede to more complex intellectual skills. Verbal or
declarative information is considered to be such thing as labels, facts, ground rules,
and bodies of knowledge. The prerequisites to vebal information leaning include
meaningfully organized sets of information, language skills, cognitive strategies, and
attitudes. Simultaneous to the learning of .vebal infonnation, some lowe orde
intellectual skills are also leaned These include discrimination, concrete concepts,
and deﬁned concepts. Discrimination involves being able to tell diffeent objects
apart. Concrete concepts involves being able to identify the properties of objects. And,
deﬁned concepts is being able to produce the mearing of a class of objects, everts, or
relationships. Once these lowe orde intellectual skills are attained, the leane can
begin to do more complex highe order leaning such as rule developmeit Rules are
the knowledge of how to respond with a class of relationships among classes of
objects and everts. Rules are important because they give regularity to performance

ove a variey of situations. Finally, as the leane develops these rules, he or she can

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35
move on to the most complex skills: highe order rules and problen solving. Highe

Orde rules are complex combinations of simple rules which ear be inverted for the
purpose of solving practical problems. Problem solving is the development of these
highe orde rules without guidance. Highe order nrles should exhibit transfe of
leaning across situations. The preequisites to the developmert of rules, highe orde
rules and problen solving are the simple skills and vebal information found in the
lowe part of the hiearchy.

Gage and his colleagues bring this leaning hiearchy, attertion, and gals
togmer at the point of instruction Gage et al. (1992) write that before presentation
ofthe stimulus mateial, three thing must take place: a) gaining the leanes attertion,
b) informing the leane of an objective or goal, and c) stimulating the recall of
prerequisite leaning. This is in line with the earlie stated propose of subgoals: the
focusing and mainterance ofattertion Gage's point ofview reinforces the need to
take the leaners attertion and focus it, by stating the objective. This helps to
leanesdirectcogitiveresomcesandtodeﬁnewhat "ontas " (tousetheKanfe
and Ackerman tem) activity ought to be for this part ofthe leanring task. Thel,
theeistheneedtostirnulatetherecallofprereqlusitestoaidinleanungthiswillbe
most importartwherﬂreehasbeertimeelapsedbetweerleaningloweordesldlls
andtheleaningofhigheordeskills. Whertheyareleanedwithouttimelagthel
the prerequisites ought to be readily available. Gage's notion of hiearchical
Wgofleamnggalseanﬂrusbemdestoodasmrpmtantmmaldngﬂrebest

use of cognitive resources. Using subgoals to gain and focus attertion to

36
systenatieally maste the verbal information and lowe order skill so that the late

leaning of rules and highe orde rules is possible will bring about bette pefonnance
and leaning by the end of the training.

Thee is some enpirieal support to back this intuitive notion that prerequisite
information and skills are required for an erd perfonnance gal to be effective.
Barley and Lituchy (1991) tested goal setting models and formd one practieal
implieation oftlreir work was that for goal setting to be successful, thee is the need to
ersurethattheenployeeshavetherequisitestoperfonn Th'mspeaksdirectlytothe
peformance goals set by Kanfe and Ackerman (1989). Setting erd perfonnance or
ultimate goals early on igores the sequencing need to develop preequisites ﬁrst.
Earley, Lee, and Hanson (1990) formd that for complex tasks arrdjobs, there is a lag
between the time new enployees receive a goal and the time whel the employee has
enough skill to peform and translate the goal into action People who lacked full
urrdestanding terded to switch strategies too often, neve allowing any strategy they
tried to take hold They obviously lacked the knowledge needed to evaluate their
strategies accurately. This ﬁnding is consistent with a similar ﬁnding by Dachle and
Mobley (1973).

Not only should late leaning be enhanced by earlie work, but if resources
can be suﬁiciertly focused on a small but meaningful portion of the task, erough
resources should be available such that self-regulation is possible without being
detrimertal to peformance. Bandura and Schunk (1981) found that subgoals allowed
for bette and faster leaning, higher self-eﬂieacy due to subgoal success, and more

37
accurate self-assessment than a more "distal" goal. Schunk (1984) found bette

performance and self-efﬁcacy using subgoals, especially with a reward for goal
attainment. Stock and Cevone (1990) found that subgoals lead to self-efﬁcacy in line
with subgoal attainmert (positive or negative self-efﬁcacy depelded upon whethe or
not the subjects achieved the subgoals), and the leane had more information about
tlreirprogressonthetask. Theefore, itisquitereasonabletoerqrectleaningsubgoals
to increase the leaning of the individual, the ultimate pefonnarrce of the individual,
the use of leaning behaviors by the individual, and the self-regulation of the
individual relative to the leaning goals.
Meacognition

A second intevertion that should have a bereﬁcial effect on the use of
resources during skill acquisition, and thus on leaning and pefonnance, is
rnetacogrritive training. Metacognition is deﬁned as knowledge of; and control ove,
cognition (F lavell, 1979). Metacognitive knowledge is consideed the knowledge of
one's own thoughts, memories and capabilities. Flavell (1987) separated metacognitive
knowledge into knowledge of peson variables, task variables, and strategy variables.
Peson variables involve knowledge about intraindividual and inteindividual
diﬁ'eences in cognitive propelsities, eapabilities, and aptitudes. For example, the
knowledge that one's own rmth skills are bette than one's verbal skills, or that one's
vebal skills are bette than someone else's would be intra- and inteindividual
metacognitive knowledge respectively. In addition, F lavell discusses univesal
metacogrritive knowledge. This is knowledge of what is true for all people. For

003mm

38
example, the knowledge that human short term memory capacity is sever plus or
minus two units. Metacognitive task knowledge is the knowledge about the demands
and constraints of diffeent tasks and infomration ercourrteed Examples of common
nretacognitive task knowledge we acquire are such thing as the knowledge that
derselypackedortechnical informationismorediﬁiculttoprocessthanothetypesof
nrateial (likeﬁction); orthatitis easieto getthegistofastorythenmemorize it
vebatim . Through expeierce people lean about the demands of dilfeent types of
tasksardmusttakeﬂresedemandshrtoaccomuinordemachieveﬂleirgals.
Finally, knowledge of metacogrritive strategy is the knowledge of techniques for
monitoring and controlling cognition Metacognitive strategy ear be distinguished
ﬁom cognitive strategy in that a cognitive strategy is enacted to get one to a cognitive
goal orsubgoal,wheeasametacognitivestrategyistomonitoranddirectthe
cognitiveprogresstomakesmethegoaliswellmet Forinstancetogetthesumofa
column of numbers the obvious cognitive strategy is to add them. A metacognitive
strategymightbetoaddthenmnbesasecondtimeorevenathirdtomakesmethe
goal has in factbeermetconectly. Addingisacognitive strategy, butre-addingto
double check is a metacognitive strategy.

The counterpart to metacognitive knowledge is metacogrritive control or
executive control (Kluwe, 1987). Metacognitive control includes such behaviors as
classiﬁeation, checking, evaluation, and anticipation Classiﬁeation, elsewhee called
monitoring, provides information about the status, type, or mode of cognitive activity
and arrswes the question "What am I doing here?". Checking provides information

39
abouttlrestateofthecognitivesystenandactivityinordetoanswethequestion

"How am I doing?" relative to a desired state which will result in metacognitive
knowledge. This is equivalert to Kanfe and Ackeman's self-evaluation, except that it
is speciﬁc to the evaluation of cognitive activity as opposed to motor activity or
performance. Anexampleistheassessmentapesonmightmakethattheyarenot
thinking clea'ly because they are tired Evaluation, is similar to checking, but Kluwe
assets that it goes beyond checking in providing infomration about the quality of
cognitive activity because criteria are actually applied in evaluation For example, a
pesonnriglrtsay"myplanisnogoodbecauseitfailstoruleoutarryris ." In
addition to these activities, Kluwe posits that people undetake the regulation of
processing capacity. People must decide to what to devote resomces, and _how much
of their capacity to devote. In orde to make good use of one's cognitive capabilities,
resources must be focused on task relevant information with erough resources to
successfully conrplete the task

Research on rnetacognition indicates that people diﬁ‘e as to their meacogitive
skills and capabilities, and that these diﬁ‘eerces are not simply a refection of
diffeences in cognitive ability (August, Flavell, & Cliﬂ, 1984; Swanson, 1990).
Expet-novice reseach suggests that expets have bette domain speciﬁc metacognitive
skills than novices. Not only do experts have more or bette strategic knowledge, they
are bette able to evaluate their strategies. Etelapelto (1993) fomrd experienced
compute prograrmnes to have bette metacogitive knowledge of strategies, bette

metacogrritive control, and bette metacognitive on-line awareress than novices.

40
Etalapelto formdthaterqretprogramesweemoreabletoidentify agoodorideal
strategy for compreherding a program than novices, and expets idertifying an ideal
strategyaremorelikelytousethisstrategythanarenovices suggestinganideal. In
addition, expets wee more aware of which strategies they were using, in tint their
reported strategies wee more likely to match their the actual strategies used
Research shows that relative to novices, expets are more likely to discontinue
unsuccessful problem-solving strategies (Larkin, 1983) and are more accm'ate in
judging problen diﬁculty (Chi, Glase, & Rees, 1982). Good reades have beer
found to be bette at comprehersion monitoring while reading than poor reades
(Bake, 1989; Pressley, Snyde, Levin, Murray, & Ghatala, 1987; for a review on
metacognition and adult reading see Rinehart & Platt, 1984).

Metacognition is not just a result of learning, meacogitive skills have beer
fomrdtobecrucialtoﬂreprocessesofleaningitself Goodleanestreatleaningas
a purposeful, attention-directing, self-questioning act (Ganz & Ganz, 1990).
Aldeman, Klein, Seeley, and Sandes (1993) reported that journals of unsuccessﬁrl
studerts indicated that they lacked metacognitive knowledge - the studerts did now
know why they wee failing, and felt that they new the material bette than they
demonstrated Improving students, on the othe hand, demonstrated the cleaest
metacognition in their jommls, focusing on evaluating their knowledge and strategies,
providing eviderce that metacogrrition is an important part of the leaning process.
Swanson (1990) found that subjects scoring higher on a metacognition questionnaire
wee able to delve solutions to basic cherristry and physics problems using fewer

(1993

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41
steps and with more eﬂicient solution rates regardless of their cognitive ability.

Pintrich and DeGroot (1990) found that self-reports of metacogrritive activity during
leaning wee positively related to academic performance in terns of seatwork,
exams/quizzes, essays/reports, and aveage grade. Likewise, Pokay and Blumerfeld
(1990) found self-reported metacognition to be related to end of semeste achievenent.
As a variable important to leaning, metacognition may be useful for traines.
Recently, training researchers have beglm to examine the use of metacognition
as away to evaluate and eihance training. Kraige, Ford, and Salas (1993) suggest
that since (domain speciﬁc) metacognitive skills are correlates of skill developmert,
these rnetacognitive skills ear be used as indices of leaning for training evaluation
purposes. Ford and Kraige (1995) propose that metacognition should be consideed
and applied at seveal stages of training: needs assessmert, design, and transfe. For
example, needsassessmerrt should identifywhichcues incurnbertsuseinordeto
know when to apply their knowledge and skills. This ear help to avoid production
deﬁciercies in which the peson knows how to do the task, but lacks the
metacogrritive skills that facilitate access to and use of this knowledge. Training
desi g can ercourage metacogitive developmert by including metacognitive
objectives, encouraging self-directed learning and allocating time during training for
trainees to reﬂect on their leaning. Finally, metacognition must be fosteed which
will allow gerealization of knowledge and skills to the job. This includes
encouraging active self-monitoring and hypothesis/strategy testing, as well as avoiding
continual feedback which ear interfee with the developmert of metacogrritive skills

42
such as self-assessmert.

Thus far, the training literature has tended to focus on metacogition as an
index of leaning - as something developed during training. The educational research
cited earlie, howeve, denonstrates that the metacogrritive awareness and skill is
important to the leaning process. Thus, the metacogrritive knowledge and control
possessedbythetraineeatthebeginningoftrainingrmy irnpacthowwellheorshe
leans. The training liteature, howeve, has not a yet addressed metacognition as an
antecedert of training success, nor attempted to manipulate metacognition prior to skill
acquisition In fact, while the training literature has acknowledged metacogition as
an important cognitive variable, the liteature's treatrnert of metacognition has beer
primarily theoretieal. Empirical reseach is needed to demonstrate whetlre or not
enhancing the developmert of metacognition will erhance the eﬂiciercy or
effectiveness of training.

Having knowledge of, and control ove, one's cognitions allows one to alloeate
and use one's cognitive resom‘ces optimally. Kanfe and Ackeman, as well as Kuhl
and Koch (1984) and F. Kanfe and Steverson (1985), indieate that self-regulatory
activity competes with on task activity, such as peforrning task functions or leaning
activities. Howeve, recall that Kanfe and Ackerman (1989) indicate that wher self-
regulatory activity yields a net increase in resources devoted to the task the results
should be beneﬁcial to the leane. This is one reason metacognition is important to
the leane. Metacogition may be used to ersure that the beneﬁts of self-regulation

outweigh the costs. The peson can only devote enough resources if the peson

43
accurately gages task diﬁiculty relative to their capabilities. Meely throwing
resources at a task does not necessarily detenrine success — the way in which the
resources are used is also important. Metacognitive regulation in conjtmction with
metacogitive task knowledge allow one to evaluate what part or aspect of the task
demands more attertion so that resomces are not wasted on irrelevant knowledge,
information, or aspects of the task Metacogrrition also allows one to monitor the
strategies selected so that resources are not wasted on behavior that is ineffective or
insuﬁciert to complete the task

Theefore, teaching metacogrritive skills holds promise as an intevertion ﬁom
a limited resources viewpoint Making these metacognitive skills more ﬁequent, and
perhaps more eﬁiciert and effective , can help the leane to more accurately gauge
what deseves the investrnert of resources. It enables the leane to have a clea'
picture ofwhat aspects ofthe task will require resources, what strategies will be
eﬁ'ectivebasedontheseresomcerequirements, andwhatareasofthewillneedmore
attention. The leanecanthususewhatresorn'cestheyhave devotedtothetaskmore
eﬁiciertly, and gain more ﬁorn their self-regulatory behavior.

The following section preserts the conceptual model developed for the current
study. This model speciﬁes the linkages among the constructs examined Speciﬁc
hypotheses deived ﬁom this model are discussed

AW

Figure 1 presents a conceptual model developed for the current study. This

model adds to the training liteature in a number of ways. First, the model

44

Conceptual Model

 

 

 

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Figure 1-- The Conceptual Model

 

 

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45
denonstrates the way in which the two intevertions discussed earlie, sequercing

training through subgoals and metacognitive training, impact the acquisition of a
complex skill in terms of both knowledge and performance by improving the way in
which on task resources are used Second, this model suggests increased speciﬁcity in
what Kanfe and Ackeman (1989) have called "on-task resources." The model
suggests two types of activities to which the resources allocated to skill acquisition can
be devoted: self-regulatory activity (including metacognition), and leaning activities.
This model suggests that by helping leanes to allocate their resources among these
activities eﬂicieltly, and in prope sequerce, the intevertions can aid in the
developmert of knowledge and performance. This section is an attempt to clarify the
activities and pmposes to which attertion is devoted during the task
WW.

These activities are the processes which explain how resources affect leaning
Orr—task resources are those resomces devoted to acquiring the skill in question These
amount of resources devoted is detemined by pefonnance-utility allocation policies
prior to ergaging in the task as discussed by Kanfe and Ackeman (1989). That is,
the individual detemines the amount of resources to be devoted to the acquisition of a
skill, and these resources are then subdivided between the various "on-task" activities
asrequiredbythetasks. Whileitmaynotbepossibleorevernecessarytodetemine
the exact amount ofresources devoted each and every activity in which the leane
ergages, it is instructive to conside the types of activities that make use of "on-task"

resources. To do so will clarify haw interventions that ﬁee resources will erable the

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Tactics

leane to lean and perform

The activities to which individuals can devote "on-task" resources which aid in
leaning are comprised of two pﬁnmry categories. The ﬁrst of these categries is
temed leaning activities and include behaviors for the purpose of ercoding, storing,
and organizing knowledge and productions. This eategry includes task speciﬁc
behaviors for the purpose of progressing through the task itself toward what has beer
called ultimate or end perfonnance. The second category important to leaning is self-
regulxory activities, be they cognitive or metacognitive. These seve as support
mechanisms monitoring and evaluating progress toward the goal, allocating resources,
and evaluating and selecting strategies for gal attainmert.
W. Learning activities are those activities to which resources are
devoted that aid directly in the ercoding, storage, organization, and retrieval of
knowledge. Some authors have refered to these activities as primary leaning
strategies or leaning tactics (Danseeau et al., 1979; Danseeau, 1975; Wade, Trather,
& Schraw, 1990) though distinctions have been made betweer tactics and strategies.
Tactics are individual techniques wheeas strategies are collections of tactics employed
in a leaming situation, perhaps necessitating irrtertion and purpose (Wade et al., 1990;
Dery & Murphy, 1986; Paris, Lipson, & Wlxon, 1983). The term "primary"
distinguishes leaning strategies that aid directly in the comprehersion, ercoding,
storage, and retrieval of knowledge from other activities which may indirectly assist in
learning (such as self-regulatory activity) which are ‘ temed supportive strategies
(Darrsereau et al., 1979). Both primary leaning strategies and tactics are subsumed

47
unde what is being refered to as leaning activities. The important featme is that
leaning activities are urrdetaker by the leane for the primary purpose ofacquiring
knowledge.

Literature investigating the strategies and tactics used to gain declarative or
vebal knowledge has focused on leaning ﬁom lecture or text sources. This reseach
suggests many leaning tactics and strategies used to learn infomration Recertly,
reseach supports the notion that leaning activities cluste in ways consistert with the
theory that these strategies developed to help compensate for the limitations of humans
as information processors. For example, leames compensate for limited processing
eapacity with integration tactics that chlmk or link information. Likewise, leanes
cornpenate for the limited durability of information being processed by repetition,
reheasal or reorganization (DiVesta & Morera,1993). While the speciﬁc names
giver to leaning activities vary, there is consideable convegelce as to the types of
leaning activities obseved in leanes of vebal information Leaning activities
commonly obseved or reported are: reading, reading slowly, mertal repetition,
imaging (creating a mertal image), using analogies, summarizing, paraphrasing, listing,
notetaking, organizing, charting, outlining, associating with previously acquired
information, nrnenonic devices, developing questions, restating in one's own words,
linking ideas that relate in the material, marking important material by lnrdelining or
highlighting, and thinking about how to apply information (DiVesta & Morero, 1993;
Nist, Simpson, & Hogrebe, 1985; Simpson, Hayes, Stalrl, Corrne, & Weave, 1988;
Spring, 1985; Thomas, 1988; Wade et al., 1990).

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In addition to the activities used to lean declarative information, individuals
will also devote resources to behaviors directed towards acquiring procedural
knowledge, rules, or productions in skill acquisition Consistent with Gage's theory,
Anzai and Simon (1979) found that subjects do in fact gereate rules based upon
expeierce with the task using information ﬁom prior learning and peception of
current problem conditions. While the speciﬁcs of the strategies for leaning rules will
vary consideably with the task, thee are strategies that leames ear use to gain
tmdestandingoftheprocedmalaspectsofthetask Somestrategiesthatmaybeused
to gereate rules are mears-erds reasoning, experimertation by purposeﬁrlly making
mistakes, experimertation by purposefully performing diffeent behaviors or choosing
diﬂ'eent options to add variability to a task The leaning activity directed at
procedural leaning may be differertiated from simple task-function activity by
diffeerces in task behavior such as increased variability in the selection oftask
practiceoptions, morepracticeofbehaviorsthatwill leadtosuccessinthelongterm
(Ford, Smith, Weissbein, & Gully, 1995), less taking the task to its compleion, and
moretimetoconrpletetlretask Theactivityisless directedatattaining high
performance, and more directed at acquiring knowledge and a deeper understanding of
the relationships among task elements.
Self-WM. Kanfe and Ackeman (1989) treat resources devoted to self-
regulation as neither on-task nor off-task In the current model, self-regulatory activity
is being consideed as a consumer of "on-task" resources since self-regulation

competes with the resources devoted to acquiring and pefonning the skill. If

49
resources are either on-task or off-task (and, really, these are mutually exclusive

categories) than the self-regulatory activity involving activities demonstrated to be
important to leaning must be considered to be part of the on-task resources. While
they are on-task resources, they are not primary learning activities. Self-regulation
activities are consideed separate unto thenselves, but playing a vital supporting role
in the leaning and peforming of skills. For eviderce tint self-regulatory activity
plays a supportive role as opposed to a primary role, one need only conside Kanfe
and Ackerman (1989) in which they denonstrate that when thee is competition
among activities, self-regulation diminishes in favor of othe activities.

Just as gals can be set eithe around leaning or perfonnance, self-regulatory
behavior can focus on leaning or pefomrance. Metacognitive self-regulatory
activities can involve the evaluation of one's own state of leaning, evaluating the task
demands, and evaluating one's leaming strategy (Bienan-Copland, 1994; Daneeau,
1979; Flavell, 1987). Since a leanring gal is to reach a cognitive state whee the
informationandmlesaredeveloped, encoded, organized, stored, andeanberetrieved,
awareress and on-line evaluation of whethe this gal is being met is metacognitive.
Anexanrple is a leane's self-assessmertthathe/sheisreadytotakeatest, ortheso-
ealled "judgments of knowing" or "feeling of knowing" (Nelson, Durrlosky, Graf, &
Narels, 1994). Thee are many tactics that have beer idertiﬁed which may be
performedtoaidleanes indetenniningtheresomcedemandsofthetaskandto
assess whee they have breakdowns in their learning. For example, surveying the

material to deternine what may be difﬁcult to lean, self-testing, or reciting material to

selectlt

50
look for holes in one's leanring are just a few examples of activities urrdetaker to aid

in self-assessmert and regulation (DiVesta & Morena, 1993; Nist, Simpson, &
Hogrebe, 1985; Simpson, Hayes, Stahl, & Weave, 1988). These metacogrritive
assessmert activities are so accepted by the educational literature as important support
strategies to leaning that many of them are built into learning/study programs like
SQ3R (Peterson, 1941) or PORPE (Simpson, Hayes, Stahl, & Weave, 1988).
Likewise, Thomas (1988)includes self-assessmert, cognitive monitoring, and strateg'
selection as important components in his model of an ideal studert.

Self-regulation also plays an important role in pefonnance across a variety of
tasks. The self-regulatory activities of monitoring pefonnance, assessing clnrent
status relative to one's gals, and reacting to this status appropriately are an important
part of reaching perfonnance gals given suﬂiciert resom'ces. Activities such as
obtaining or checking feedback and reviewing the goal are important activities for
determining how pefonnance is progressing, and evaluating the required eﬂ‘ort
Metacognition plays a lesse, but important part in this self-regulation through strategy
evaluation and selection Effective leanes and perfonnes must check their progress
andassesstheirstrategytoensrrretlratitiseﬁective. Failurestochangestrategiesor
changing strategies too soon can result in poor perfonnance (Barley, Connonlly, &
Ekegrer, 1989). Self-regulatory behavior, be it cognitive or metacognitive, plays an
important role in support of both leaning and performance activities involved in skill

acquisition

51
Linkage
Based upon the supportive liteature discussed thus far, the current model is

presented to clarify how sequencing training through subgals and metacogrritive
training ear aﬁ‘ect leaning during skill acquisition The model suggests that skill is
detemined prirmrily by the acquisition of knowledge (in terms of declarative
knowledge and procedural knowledge). Knowledge acquisition is affected directly by
the amount of learning activity and metacognitive activity pefonned by the individual.
Thisleanirrgactivityisdeterrninedbythetypeofgalthelearneisplnsuing, beit
arroutcomeoriertedgal, orasetofsequercedsubgals. Theamountofleaning
focused self-regulatory activity the leane ergages in is detemined by both the type
of gal, and whethe or not the leane has received sorrre nretacogrritive training prior
to skill acquisition Therefore, the effects of metacognitive training and gal type on
krrowledgeand skillareexpectedtobemediatedbytheamormtofleaningactivity
and metacognitive activity. These linkages are explained below, and speciﬁc
hypotheses are oﬁ‘eed

In their review ofthe gal setting liteature, Iccke, et al. (1981) indicate that
oneofthemajorpurposes ofagal istodirecteﬂ‘ort. Inshort, peopleputeﬁ‘ort
toward behaviors for which they lave a difﬁcult, speciﬁc gal more than behaviors for
which they have no gal, or a "do your best" type of goal. People with peformance
galswill devoteﬂreirresomcestoacﬁviﬁesﬂrataeconsisteuudﬂrﬂlistypeofgoal.
This means that they will have to divide their reoruoes between a number of different
activities. People with performance gals will direct their effort toward achieving the

52
ﬁnal performance goals, but must concmrertly attempt to learn the declarative

knowledge and procedures needed to perform, self-regulate to the gal, engage in
metacognition regarding their learning, and perform the task as best they can Thus
thee is consideable division of the resources. This division of resources means that
the individual has fewe resources available to devote to leaning activities which are
intended speciﬁcally for the ercoding and storage of declarative information, or the
gereation of procedural if-ther rules. Individuals with pefonnance gals are likely
torelyonrepeatederqrosmetoﬂlemateialtobﬁngabomleaningratheﬂrar
devoting resources to leaning activities.

On the other hand, it is possible to set subgals which sequerce training for
the leaner in light of instructional sequencing theory. One advantage to be gained
using subgoals is that the leaners resources are focused onto a smalle portion of the
task This should to lead to bette pefonnance, and more self-regulation since more
resomcesarefocusedonwhatamormtstoasmalletask Thisissirnilartotheidea
behind part task training (Naylor, 1962) in that the task is, in effect, broker down into
subtasks with each smalle subtask able to receive the leanes full attentional
resources. Part task training generally involves practice on some subset of task
conrponerts prior to practice on the whole task (Proctor & Dutta, 1995). Wigtrnan
and Lintem (1985) identify three basic methods of task decomposition for part task
training: segrentation in which the whole task is broker down along spatial or
temporal lines, fractionation which involves breaking a task into components
perfonned concmrently, and simpliﬁcation in which training is performed on a

53
simpliﬁed vesion of the task

Subgoals and part-task training operate to some degree on the same principles
inthatthetaskisineﬁectdividedupsothatresomcescanbedevotedtoasmalle
portion Howeve, sequercing subgals do not involve a previous practice period
Subgals are set prior to practice on the whole task to direct the effort ofthe practice.
In addition, sequercing subgoals do not divide the task along the dimersions such as
time, corrcurrerce, orspace. Insteadthesubgalsdividethetaskaccordingtothe
sequertial leanring tasks between initiating leaning and autonraticity that nrust be
accomplished, i.e. gaining declarative knowledge, gaining procedural knowledge, and
ﬁnally performing at a supeior level. Theefore, sequercing subgals are not
equivalert to traditional part-task training. In addition, subgals should offer
advantages ove simple part-task training on a rmmbe of counts. First, sequercing
subgalsrequirempreuainingwhicheanbecosﬂyintemsofﬁmeardequipmeu.
Secondly, subgalsdonotbreakthetaskapartintosmallbehavioral conrponertsthat
must be re-integrated, but sequertially guide the acquisition of knowledge which
builds upon itselfto create fast, accurate perfonnance. In this way, sequercing
subgals should avoid the diﬁiculty pesons trained using some part-task methods
ercounte wher attempting to recombining the various task componerts on the whole
task, particularly for complex task with high component integration (Naylor and
Brigg, 1962).

By focusing resources on a snralle portion of the task, sequerced subgals
allow for more learning activity. Ratlre than asking the leames to perform multiple

54
tasks simultaneously - ercode declarative information, detemine task procedures,

perform at a maximal level, and self-regulate -— sequerced subgals divide the task
into more manageable parts for the leane. The main hurdles in skill acquisition that
must be accomplished prior to smooth accurate performance are leaning declarative
and procedural knowledge (Andeson, 1982; Kanfe & Ackeman, 1989; Gage, et al.,
1992). Subgals should be set to direct the leane to focus all oftheir resources on
sequerced steps to attaining smooth accurate pefonnance. The subgals allow the
leane focus on the sequertial leaning tasks and to build upon previous leaning as
they progress. Rothkopf and Billington (1979) demonstrated that leaning gals wee
useful in the directing of effort. Likewise, Hoﬁnann (1993) suggests that leaning
gals help to avoid detrimertal cognitive intefeelce, such as that which occurs when
the leane is faced with nurneous competing dernanck. Subgals ercourage leanes
to ﬁrst lean the necessary declarative knowledge, ther the procedures, and only ther -
-aﬁeasolidfomrdationhasbeerlaidandresomcesﬁeedﬁomthisleaning—are
pefonnance gals giver. This sequercing of gals ercourages leaning activity by
placing the primary focus early in skill acquisition on leanring not pefonnance, and in
directing the allocation of resources to activities which enhance leaming. In addition,
propely sequenced subgoals divide the task into more manageable subtasks, making
the leaning needed to meet each gal manageable. This should to ﬂee erough
resources for the necessary leaning activity. These subgals are arranged such that
late gals take advantage ofthe resources freed due to prior leaning. Theefore, it is
hypothesized that the amount of leaning activity urrdetaken by the individual will be

55
affected by the type of goal they have been assiged Stated explicitly:

methﬁiil: People giver subgoals will engage in a signiﬁcantly greater

amount of learning activity than will those pursuing performance gals.

In keeping with the distinction made between primary and supportive activity,
in the currert model self-regulatory activity is consideed separately as activity which
plays an important support role in learning and peformance. Metacognitive training
should enhance on-task metacognitive activity devoted to leaning. By making
subjects more aware of the importance of metacognitive activity to leaning, these
skills should be viewed as worthy of resource devotion Plus, as with any skill
containing consistert elemerts, providing practice in important metacogitive skills
slmulddecreaseﬂrecogrﬁﬁveresomcesﬂlataereqluredmpefonnsmhself-
regulatory activity (Norman & Bobrow, 1975; Shiﬁiin & Schneide, 1977). Seveal
authors including F. Kanfe and Stevenson (1985) and Kanfe and Ackerman (1989)
have suggested that training in self-regulatory activities may reduce the amount of
attertional resources required by these self-regulatory activities. A third potertial
beneﬁt of training is that it may improve the quality or accuracy of metacognitiVe
activities.

Because metacognition plays a large role in leaning, training in these skills is
likely to result in increases in metacogrritive activity in support of learning. Indeed

the literature on metacognition suggests that it is an area in which improvenert is

56
otter necessary. While metacognition develops in all individuals until the erd of
elernertary school and pehaps beyond (Brown, 1978; Fingerrnan & Pelrnutte, 1994),
metacogitive skill may vary more betweer individuals than othe cognitive variables
like memory, and may be a major determinant ofsuccess in seveal skill areas
(Redding, 1990). In addition, research indicates that these metacognitive differences
are not meely accomted for by diﬁeerces in ability, as they exist ever afte
controlling or matching for ability (Angst et al., 1984; Swanson, 1990).

Efforts to train metacognition has produced ercouraging results in educational
setting (Ryde, Beckchi, & Redding, 1988). Redding (1990) concludes that
metacognitive skills can be readily taught and leaned To wit, nrarry studies have had
success improving learning or performance by training metacognitive skills, or using
intevertions to increase metacognitive activity (Pressley, et al. 1987; Bear, Singe,
Sorte, & Frazee, 1986; Lorerc, Sturmey, & Brittain, 1992). While some researche
are of the opinion that to train a cognitive skill completely requires long tern training
ove weeks or months, snralle shorte intevertions have prover effective at
increasing nretacognition and subsequert pefonnance (Pressley et al., 1987; Lomenc
et al., 1992). Theefore, training individuals to be aware of their own metacognitive
processes, and to use these metacogrritive skills to aid leanring will reduce the
resources needed to peform these activities, increase the use of these activities, and

increase the skill with which they are used. The next hypothesis follows:

57
W112: Metacognitive training will be positively related to
metacognitive activity pefonned in support of leaning. Individuals giver such
training will report and denonstrate greater use of metacognitive activity to

assess and optimize their leaning.

Anothe variable expected to have impact on the amount of metacogrritive
activity is gal type. It is likely that those with subgals will perform more
metacogitive activity, and those with pefonnance gals will perform less. This is
the case for two basic reasons. First, pefonnarrce gals both direct the individual
away ﬁom leaning activities, and spread the resomces of the leane across a nurnbe
of diffeert activities. In attempting to perform on the task prior to compilation one
mustlean infonnationnecessary forperfonnance, attempttoperformonthetask, and
attempt to self-regulate. This spreading of resources rmy create a situation in which
thee are insufﬁciert resources available to allow metacogrritive activity through much
oftlretaskarrditwillbediminishedinfavorofotheractivities. Asecondreason
individuals given a pe'fomrance goal may not ergage in as much metacognitive
acﬁvityisthatﬂrepefomrarcegalsﬂreyaegivendonotdirectﬂrenresomces
toward leanring, acquiring information, or assessing their state of knowledge.
Pe'formance gals are likely to lead to self-regulation (wher it occurs) around
performance itself; and not assessmert of one's own knowledge or strategy.

In contrast, individuals receiving sequerced subgals should ergage in more

metacogrritive activity. Subgoals focus on leaning and thus should direct attertion

58
toward leaning activities, and self-regulation that supports such leaning. Not only

should subgals focus the individual on leaning, but more resources should be
available to allow for such regulation Since subgoals focus on a particular subportion
of the skill acquisition task, and the subgals are sequerced to allow prior leaning to
ﬂee resources late in skill acquisition, the learne will have more resources available
to devote to self-regulation at each stage, early as well as late. One can intepret
Kanfe and Ackeman's (1989) third study in this light. They trained the subjects on
eithe declarative or procedural part task training and both wee effective in bringing
about improvements in score which indicated that the people learned the part task
training and could apply this leaning to the task In effect, the authors increased
performance by setting subgals for a particular part of the task

Not all subgoals are equal. While both declarative and procedural training
increased subjects' score, only declarative training ﬁeed erouglr resources to allow
self-regulation regarding the erd pefonnance gals set by the authors. This is
probablybecausetheprocedural galwasoutofsequerce, inordetopeformthe
subjects needed to devote resources to learning the declarative information before
procedural leaning could ﬁee enough resources to ercourage self-regulation The
resulting hypothesis is:

Will: Sequenced subgoals lead to more metacogitive activity in

support of leaning than will performance goals.

The previous two hypotheses suggest that it is possible that metacognitive

59
training and gal type will inteact to determine the amomrt of metacogrritive activity
that takes place on the task. That is, although metacognitive training is expected to
increase this activity, the type of goal one has may act to determine the effectiveness
of this training. Those with only metacognitive training would be expected to
demonstrate increased metacogrritive activity giver this training. Sequerced subgals
that focus resources on learning should yield more ﬁee resources faste. These free
resources are available for devotion to metacognitive activity. Theefore, those with
subgals should bereﬁt more ﬁom metacogrritive training since they have the
resources available to transfe this training to leaning on the task In contrast, those
withperfonrmnce goals aredirectingtheirresomcestowardpefonnance. These
individuals are in effect dividing resom'ces among the aforernertioned activities
leaving insuﬁiciert slack mom to devote to self-regulation Thus, the
metacogitive training they receive will have no effect on the amount of nretacogrritive
activity in which they ergage.

Howeve, this hypothesis is more exploratory because individuals may react
diffeerrtly to the performance gal-metacognitive training combination Metacognitive
training is encomaging them to expend resources on leanring supportive activities,
while the perfonnance gals are encomaging them to devote their resources away ﬁom
leaning activity and leaning supportive self-regulation in favor of peformance
activity and performance supportive self-regulation Some learners may react stronger
to the performance gals and thus the potential inteaction is expected Howeve,

others may react to the training by ergaging in more metacogrritive activity despite

60
their pefonnance gal. This would be expected to have negative impact on the

eﬁ'ectiveless of their performance goal, and a yield a modeate amount of
metacogrritive activity.
WM: Metacognitive training and gal type will inteact to determine
the amount of metacognitive activity devoted to learning. Those with
sequerced leaning subgoals will ergage in more metacognitive activity as a
result of training, those with performance gals will not denonstrate an

increase in metacognitive activity.

Thenextlinkagesproposedbythismodelaeﬂrelinkagesbetweerleaming
activity and nretacogrritive activity with the acquisition of knowledge. The on task-
activities outlined hee are each expected to relate positively to the acquisition of
knowledge, both declarative and procedural. Leaning activities are performed
speciﬁcally with the intertion of increasing knowledge. These activities aid directly in
the ercoding, storage, organimtion, and retrieval of knowledge. Devoting resomces to
studying, memorizing, developing nmenonics, and othe such leaning activities are
expected to increase declarative knowledge. Similarly, exploring the task to gain an
tmdestanding of the relationships betweer task elemerts is expected to increase the
procedural knowledge associated with undestanding if-ther relationships.

61

 

 

 

 

 

Sequenced
Metacognitive Goals
Activity /
Performance
Goals
No Metacognitive
Metacognitive Training
Training

Figure 2 -- Theoretical Relationships For Hypothesis 4

62
W5: Increased leaning activity will lead to increased leaning in
terms of declarative knowledge, procedural knowledge, and skill performance.

Based upon the previous review of the liteature, metacognitive activity is also
expected to increase leaning in terms of declarative and procedural knowledge. As
discussed earlie, metacognition has been found to be related to leaning in a nurnber
of studies both in the classroom (Sinkavich, 1990; Aldeman, Klein, Seeley, &
Sandes, 1993; Nelson, Dunlowsky, Graf, & Narens, 1994; Vadhan & Stande, 1994;
Rinehart & Platt, 1984) and out of the classroom on tasks such as leaning to use
rrredical decision making software (Ridley et al., 1992), electrician performance
(Mikulecky & Ehlinge, 1986; Garrz & Ganz, 1990), and compute programming
(Etelapelto, 1993). Metacognitive skills like self-assessmert or strategy evaluation are
importarrttolearning. Thosewhoelgageinmoreoftheseactivitiesarelikelytogain
lmdestandingregardingwhee gaps intheirlearningexist, howto ﬁll them, arrdwhen
a leaning strategy is not animal. These advantages to performing metacogrritive
activity should ultimately enable pesons ergaged in more metacognitive activity to
have more complete knowledge of the task

W: Metacognitive activity will be positively related to knowledge.

People ergaging in more metacognitive activity will demonstrate increased

declarative and procedural knowledge.

Finally, the current model suggests that this increase in knowledge is

lifr’ptﬂle
Kraige
cognitil
multidh
bowler

skﬂlba

 

63
hypothesized to translate into increased performance on the skill being leaned

Kraige et al. (1993) suggest that leanring is not unidimelsional, but consists of
cognitive, skill based, and affective dimersions. The cunerrt model acknowledges the
multidimersional nature of pefonnance, and suggests that the development of bette
knowledge based outcomes (declarative and procedural knowledge) will lead to bette
skillbasedoutcomes. Thisistlrecasefortworeasons: l)skillbasedoutcomessuch
as reaching automaticity necessitate successful compilation of the declarative
knowledge and procedures making the acquisition of this knowledge vital to rapid
pefonnance, and;2)itisnoteloughtoautonntizeanyprocedures,thebest
pefornres will automatize the most efﬁcient and accurate procedures. Athletic
coaches lmve acknowledged this truth by updating the trite saying "practice makes
pefect" to "perfect practice makes perfect". The leanes must gain elough
knowledge to elable the automatization of the fastest most accurate procedures
possible. Particularly for more cognitively complex tasks, the degree to which one has
knowledge of the task will determine how effectively the skill can ultimately be
performed Many studies have veiﬁed the knowledge to performance relationship
across a wide variety of tasks. As studies of job performance have attested, job
knowledge is one of the best predictors of performance (Hurlte, 1986) .
W: Knowledge will be positively related to skill performance.
Individuals demonstrating hi ghe levels of declarative and procedural
knowledge will exhibit better performance. %

64
The currert model also suggests that the effects of metacognitive training on

knowledge acquisition are fully mediated by the metacogrritive activity denonstrated
by the leane. That is, metacognitive training should increase the amount and quality
of metacognitive activity on the task This increase in metacognitive activity will in
turn impact knowledge, and knowledge will impact skill. Metacognitive training is not
expected to have any direct impact on knowledge acquisition or skill performance
othe than the inﬂuerce of this training on rnetacogrritive activity.
Hypnthesisﬁa: The effects of metacognitive training on knowledge acquisition
will be fully mediated by the impact of this training on metacognitive activity.
Metacognitive training will not directly affect the acquisition of knowledge.
Hymthesisjb: The effects of metacognitive training on skill pefornnnce will
be fully mediated through effects of this training on metacogrritive activity, and
metacogitive activity’s affect on knowledge development

Likewise, the effects of gal type on knowledge acquisition and performance
are expected to be mediated through their effects on metacognitive activity and
leaningactivity. Thatis, goaltypeisnoterqrectedtohaveadirectimpacton
leaning nor performance other than the inﬂuelce which comes through gal type's
effects on leaning activity, and metacognitive activity, and their effects on knowledge

acquisition, and ultimately skill. Thus the ﬁrm] hypotheses are offered:

65
W: The eﬁ‘ects of goal type one lorowledge acquisition will be

fully mediated by the effect of goal type on the leaning and metacognitive
activity. Goal type is not expected to exhibit direct effects on knowledge
acquisition or pefonnance.

Hypgthesisjh: The effects of gal type on skill pefonnance will be fully
mediated through effects of this training on leanring activity, metacogrritive
activity, their effects on knowledge developmert. Goal type is not expected to
exhibit a direct effect on knowledge acquisition or pefonnance.

METHOD
SamplcandDesign

Participants were undegraduates at Michigan State Univesity errolled in
introductory psychology courses. They received extra credit for their participation in
this experimert.

The study is a 2 (gal type) X 2 (metacognition training) ﬁllly crossed factorial
design The two levels of goal type are sequenced subgoals and perfonnance goals.
The second factor reﬂects the presence or absence of metacognition training prior to
task ergagement

A powe analysis was conducted to detennine the sample size required to
detect a modeate effect size with a powe of .80 and a signiﬁcance level of .05
(Cohel, 1977). For a 2 X 2 factorial design, cell sizes of 20 will result in a powe of
.80. Theefore, the goal of the study will be to have 20 subjects in each cell yielding
a total sample size of 80.

Ihclasls

The task is a revised version of the computerized radar simulation named
TANDEM (Tactical Naval Decision Making System; Dyer, Hall, Volpe, Cannon-
Bowes, & Salas, 1992). TANDEM depicts targets on a radar screel. Trainees are
placed in the role of Radar Operator of a US. Navy Aegis-class cruise. Using a

mouse, the operator chooses which targets to "hook" and collects information about

66

67

the target ﬁom pull down menus. This information is compared with preset ranges
and combined in orde to classify the target's Type, Class, and Intent Having made
these classiﬁcations, the (beator must ther decide to shoot hostile targets and clear
peacefultargetsﬁomthescreen. Thegaloftlretaskistoconectly select, classify
and process targets as eﬁiciently as possible. Operators must lean how to prevelt
targets from erteing critical zones surrounding their ship. If targets are allowed to
peletrate these "penalty circles," points are deducted Individuals must lean to check
thespeedarrdrarrgeoftargetstoprioritizethernarrddeternineanordeof
ergagemelt. Subjectsarepresentedvvitlraseies ofscerariosthatvaryinthenmnbe
oftargets and proportion oftargets that threater to penetrate the pelalty circles. The
scerarios wee desiged to have roughly equal complexity in terms of numbe of
targets, nurnbe oftargets threatening the penalty circles, ambiguity in the cues, and
theneedtozoominoroutonthescreertoidertifyappropriatetargets.
W

Whersubjects arrivedtheyreadand sigedtlreconsertfonnthatdescribed
the expeimert (See Appendix A). Next, they completed a demographics
questionnaire which measured possible conformds such as erqrerierce with the task or
time spert playing video games (See Apperdix B), as well as the Wondelic as a
measure of cognitive ability to use as a co-variate since Kanfer and Ackerrran suggest
that those with higher cognitive ability have a greate pool of resources ﬁ'om which to
draw. Next, subjects in the nretacogrritive training group received the metacognitive
training(seeAppendixD). The expeimentereadtlrroughthetrainingwitlrthemand

68

took them through the execises. The control subjects did the same execises, but no
refeerce to metacognition was made.

Alte a ﬁve nrinute break, the participants began the process of leaning
TANDEM Participants started with a brief denonstration regarding how to use the
mouse and perform the mechanics of the task, including cue ambiguity and pelalty
circles.

Alter this introductory session, participants wee giver their ﬁrst gals to allow
the leaning/performance gals to take affect. Leaning gal participants wee told
thattheirjobthroughthestudysessionandnexttwotrialswastocornrnittonremory
the cue values and othe speciﬁc, relevant information Many leanring goals suffe
ﬁombeingtoogereral,thereforetheseleaminggalsattenptedtobeasspeciﬁcas
the "difﬁcult, speciﬁc" perfonnance gals. It was emphasized that leaning the
declarative information, not scoring well, was what they were being asked to do for
theﬁrsttwoscerarios. Peformancegalpeopleweetoldthattheymusttrytoreach
a particular performance goal (determined by pilot testing) that represerted the 90th
pecertile on each of the ﬁrst two sessions, and they must "prepare themselves" to do
soduringthenext fewminutes, andthenattempttoreachtlreirgal. The90th
pecertile was chosen for two basic reasons. First, gal setting research has
denonstrated that eﬁective gals are difﬁcult and speciﬁc, but not beyond. the ability
of the subject (Locke et al., 1981). A 90th pecentile gal, theefore, represerts a
level tlult ten percent of a similar population ("do your best" pilot subjects) wee able
to reach, making this gal difﬁcult but not impossible. The second reason this goal

69
was choser was to be consistent with the prior research ofKanfer and Ackerman

(1989) who used 90th pecentile scores as difﬁcult, speciﬁc peformance gals.

Afte a gal commitment measme (see Appeldix G), they wee given 15
minutes to read/study the material. They were told that they may write on the
mateial at any point during the experiment Finally, subjects had their gals re-stated
and the nretacogrritive training subjects wee reminded to try to use what they leaned
during training to help then The subjects ther ergaged in two sever minute
scerarios.

Next, the second gal manipulation was giver (see Appendix F). The
sequercedsubgal groupwas giver instructionstotryarrdleantherules oftlretask
To try and grasp "if-ther" relationships betweer task elemerts. The peformance gal
group was giver a second pefonnance gal indicating the 90th pecentile (as
detemined on a pilot test) for the task The gal commitmert measure followed gal
assignmert. The participants then performed two more sever nrinute scenarios.

Afteashortbreak, aﬁrmlsetofperformancegalswasgivertoallofthe
participants. Theyweetoldthattheirpe'fonnanceontheﬁnal scerario istheﬁnal
"test" of how well they have learned the task All participants got performance gals
that represerted the 90th pecentile for the last three scerarios. The gal conrrnitrnert
measure was given once again following this manipulation The ﬁnal two scerarios
wee sever minutes each. Perfomrarrce measures taken ﬁom the ﬁnal scenario were
considered "erd of training performance." V

Following these scenarios, participants wee given questionnaires to evaluate

70
how much learning activity and metacogitive activity they performed. Upon
completion of the measures, participants wee debriefed, giver a debrieﬁng sheet
(found in Apperdix K), and dismissed from the experiment.
3 l I I l . l .

The gal type manipulation involves the type of goal assignment that is giver
to each of the participants. Participants in the perfonnance gal condition received
goals which are difﬁcult and speciﬁc indieating a score to achieve for the scerarios
which follow. As with previous research (e. g. Kanfe & Ackeman, 1989) the gals
weesetbetweerasrnall setoftrials rathethanbeforeeachandevey trial.
Pe'formanoe goals were set to capture the 90th pecertile ofpeforrnarrce scores
detemined by pilot testing consistert the operationalization of difﬁcult, speciﬁc
performance gals used in previous research

Participants in the sequenced subgal manipulation received goals which
attempted to capture the learning sequence implied by Gage and associates (Gage et
al., 1992). First, a declarative learning gal was set. Subjects wee told to focus on
learning as best they can particular facts and information about the task. Particularly,
the subjects wee told to learn the cue values and penalty circle loeations (see
Appendix F). This gal represented a speciﬁc and moderately difﬁcult gal which
eaptures declarative learning needed for pefonnirrg the task (i.e. facts and
information).

In keeping with the hierarchical sequencing notions of Gage et al. (1992), the

secondgalforthesequencedsubgal groupattemptedtodirecttheirattentionﬁom

71
declarative lorowledge to procedural knowledge. The second goal for the sequerced

subgal group was therefore to learn the relationships betweer task elemerts. This
focuses participarrts' cognitive resources on the procedural elenerts of the task. These
subjects wee asked to deive "if-ther relationships" betweer task elemerts. To lerd
increasedspeciﬁcitytothis gal, andtoaidsubjectsinmrdestandingwhattheyare
being asked to do, a list ofseveal "if" begimrings was provided so that the
participants wee asked to ﬁnish the senterce with the "ther" half. Subjects in this
condition wee asked to write down as many of these rules as they could following the
two sessions unde this goal condition.

Finally, for their third goal manipulation, the sequenced subgal group was
giver a performance goal. This gal was identical to the gal for the performance
gal group, and represerted the erd of the progression of gals from declarative
learningtoprocedural learningtopeformance(seeAppendixF forthegal
manipulations).

I l . . I . .

The metacognitive training manipulation is directed toward teaching
participants about metacognition, emphasizing its unportance,’ and giving practice at
metacogrritive activity. The program was desiged to elucidate the issues, to clarify
then, andmakepmcessesthatlikelyhavegnermatterdedmoresaliert. Practicewas
giver so that the subjects could become comfortable thinking about these topics and
working with these issues. Ideally, this training would work toward bringing
metacogrritive procedures tlmt work toward autonmticity. Howeve, as a ﬁrst step, the

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72
training was an attempt to increase the amount and quality of metacogitive activity.
Recall that the theory predicts that sequenced subgoals should free sufﬁcient resources
for this metacogrritive activity without being detrimertal to learning and this should
bereﬁt pefonnance.

Theﬁrstpartofthetrainingwasconcenedwithteaching individualstoplan
their learning. This includes idertifying which elemerts of the task are diﬂicult and
require more resources or a diffeent strategy. Practice tasks wee giver, allowing
participants to practice idertifying which type of material would be diﬂicult to leanr.
Participants wee preserted with seveal opportunities to assess mateial to detemine
whichaspectsﬂreyﬂrinkweemostdiﬂieﬂttolemn(mrdthusreqrnreﬂremost
attertion) and explain why they make this assessmert. The training also ercouraged
individualstotlrinkaboutwhattlreycoulddotoaidinlearrringthe diﬂicultmateial.

Next, the training ercouraged individuals to evaluate their learning in term of
how well they need to know mateial against how well they actually know it. This
partoftlretrainingdirectedpeopletodeterminewhatthegaloftlreirlearningis, and
ther to make sure that they have reached this gal. Execises wee designed to help
individuals see the importance of clarifying gals and invoking evaluation methods,
like self-testing, to evaluate their learning. This was done by ﬁrst focusing participants
on assessing how well they need to undestand the material. Seveal questions wee
giver as examples of the types of questions they needed to address. Next, the training
provided the participants with two opportunities to attempt to learn information and
decide wher they knew it well erough to answe questions about the mateial. Self-

73

testing was recomrnerded as a strategy to monitor and promote metacognitive
knowledge. Questions are provided to give them feedback as to whethe they
adequately knew the nrateial, and were thus correct in their self-assessmerts.

Finally, the training ercomages participants to think about strategy evaluation.
This training ercourages the participants to: clarify what strategy they are using,
evaluate whethe the strategy is working, and consider how much time the strategy
should need to work Execises wee designed to help individuals to examine
strategies, and think about the issues regarding strategy evaluation. Participants wee
giver a problen and asked to ﬁrst generate as many possible strategies for solving the
problem as they can Then they wee asked to evaluate their strategies by ordeing
thenﬁ'ombesttoworst(SeeAppendixD).

Inordetoensuretlmtalleifectsareﬁ'omthefowsonmetacognitioninthe
metacogrritive training, and not simply engaging in the exceeises, the people in the
norr-metacognitive training conditions took part in the exact same activities as the
metacogrritive training group. Non-metacogitive training subjects wee told that these
extra activities are necessary to allow the expeiment to last as long as othe
experimental conditions so eveyone gets the same credit Participants wee told that
these exceeises are being tested for anotlre study, and the experirnerte was interested
in their opinion of the difﬁculty of the execises. No mertion of metacognition or
self-regualtory activity was made to the non-metacognitive training group.
EilQLSIndics

Two pilot studies wee conducted for this expeiment. The ﬁrst pilot study

74

focused on evaluating/ﬁne tuning the metacognitive training. Approximately ter
participants wee giver metacogrritive training. Aﬁe a task denonstration they wee
asked to study the task mateial for 15 minutes and perform the task scerarios.
During the scerarios, the subjects wee stopped evey 1.5 minutes and asked to
describe what they wee thinking. The experimerte transcribed their staternerts.
Following the scenarios, participants were giver the metacogrritive questiomraire and
asked to discuss the metacogrritive training regarding whethe it erharrced the quality
oramourrtofmetacogrritiorr, whethewastoodiﬂicultoreasy, andwhethe'itneeded
improvemert. They wee also asked if they found the metacogrritive training useful
forlearnirrgtlrematerial. Anymarksmadeonthemateial wee evaluatedasto
wheﬂrethesemmksweeuseﬁﬂtosupponselflmponmeasmesofmeacogrﬁﬁvee
learning activity. This ﬁrst pilot study indicated that subjects did not ﬁnd the initial
vesion of the metacogrritive training vey helpﬁrl, they felt it did not apply since the
mateial and task were very different. They expressed few metacognitive staternerts
during the scerarios. The metacogrritive training was rewritter, and the pilot rerun.
The subjects geneally wee more positive to the new training, reporting that it was
somewhat helpful in thinking about how to approach learning the material, and
evaluating their strategies. They appeared to mertion metacognitive activity ﬁ'om the
training more during the task, though few such statenerts wee nrade relative to target
assessmert or othe task activity. Vey little writing on the material occurred during
the pilot studies.

The second pilot study ascetained 90th percentile scores for each of the 6

75
scerarios, and assessed the timing of the expeimert. Participants wee given no
metacogrritive training and "do your best" goals. Afte an introduction and task
denonstration, participantshadﬁﬂeenminutestostudythemateial arrdther
peformed the scerarios. All measures wee giver in their appropriate places in orde
to evaluate the psychometric properties of the measures, and the timing of the
expeiment The subjects’ mean scores on the metacogrritive questionnaire wee
compared to assess whethe the metacogrritive measrne was being strongly impacted
by denarrd effects ﬁonr the metacogrritive training received by the ﬁrst group. No
such effects wee found In fact, the group without the training received slightly hi ghe
scores on the questionnaire.

The data ﬁom the pilot tests was also be analyzed to examine the reliability of
the learning activity, metacognitive activity, and goal cornrnitrnert scales. Slight
inrprovenerts wee made to these measures as appropriate.

Measures

ngnit'meAbilitx. Gereal cognitive ability was assessed using the Wondelic
Pesonnel test. This short form cognitive ability test consists of ﬁﬁy items arranged
in orde of difﬁculty. Item contert includes word comparisons, disarranged serterces,
serterce parallelism, following directions, nurnbe conrparisons, nurnbe seies,
analysis of geometric ﬁgures, and truth or logic story problems. Subjects wee giver
12 minutes to conrplete as rmny items as possible. Scores on the Wondelic test are
highly related to scores on longe tests of cognitive ability. In addition, the test-retest

reliabilities have ranged ﬁ'om .82 to .94, and intenal consistercy reliabilities (based on

76
odd—ever correlations) have ranged ﬁom .88 to .94 (Wondelic Pesonnel Test &

Scholastic level Exam User’s Manual, 1992).

Demographics. Subjects answered questions regarding their age, sex, GPA,
previous lab expeience, and previous video game expeierce (see Apperdix B).

ﬁmlﬂmmitmmt Goalwmmitrnertwasmeasmedwithathreeitenscale
using Liket type itens adapted from Kanfe and Ackerman (1989). The itens ask
about how willing the participants are to work hard, put forth effort to reach the gal,
as well as how committed the subjects are to working as hard as possible to reach the
assigned gal. Coefﬁcient alpha measures of internal consistercy reliability for the
scale reported was .61, .81, and .82 on experimerts one, two, and three respectively.
Consistertwiththescale'suseinthepast, goalcommitrnertwasmeasuredaﬁe
participants received a new gal, before ergaging the task Note that participants saw
mrdpefomedadenesuatimmdmateialmrdﬂrushadsomeerqrosmemﬂreirtask
prior to answeing gal commitrnert questions. Speciﬁc items ean be found in
Apperdix G.

Wham. Metacognitive activity was measured by a 13 iten
self-report measure developed for this study. This scale measured the extert to which
subjects performed metacogrritive activities that are important to learning and
pefonnance. These include planning, previewing nrateial to detemine resorn‘ce
requirements, marking diﬁicult mateial, self-testing and learning evaluation, and
strategy selection and evaluation. Subjects wee asked to respond on a 7 point Liket

type scale from Strongly Agree (1) to Strongly Disagree (7) with ﬁrst peson

77
statemerts regarding weathe they performed speciﬁc metacogrritive activities and
behaviors. These items are found in Appendix E.

Asreactivitymaybeaconcen, amorenon-reactivemeasurewas gatheredto
corroboratetheself—reportdata. Onewaytodothisistoaskanoperendedquestion
such as "what did you attenpt to do on the last trial?" This question was asked
following each trial so as not to induce metacogrritive activity. The arrswes wee
examined, coded by multiple codes as to the amount and quality of metacognitive
self-assessmert reﬂected in the arrswes. Inte-rate reliabilities wee ther established
See Apperdix J for the basic open ended question and answe sheet Instructions to
rates can be found in Apperdix L.

W- Self-reported use of learning activities was also measured
withascale developedfortlrisstudy. Thisscaleassessedthedegreetowhich
individuals ergaged in activities which aid directly in the ercoding, storage,
organimtion, and retrieval of knowledge. The scale is arranged in seies of Liket
type item on a 7 point scale which ask the participant to indieate the extert to which
he or she engaged in activities such as mertal rehearsal, listing, undelining, using
mnemonic devices, or associating nraterial with previously learned nmteial. In
addition, the scale asks the degree to which the participant engaged in task speciﬁc
learning such as ergaging targets just for practice (as opposed to attempting to score),
or erqaerimerting with new strategies. The speciﬁc questions are in Apperdix I.

In addition, as a way to substantiate the self-report, participants will be asked
aﬁeeachtrialwhattheyhadattemptedtodo duringthattrial. This infonnationwas

78
examined by quantifying the nurnbe of learning activity statements made. This
information was correlated with the self-report measure to corroborate the self-report

Kmmdelge. Knowledge as assessed prior to the ﬁnal scerario. Knowledge
was measured with a multiple choice knowledge test that measures declarative and
procedural knowledge. Declarative items ask for sirrrple facts or information such as
cue values. Procedural knowledge items wee designed to tap a pesons undestanding
task elenert relations using if-ther production based questions. These questions
require test takes to evaluate situations and choose the best option. The knowledge
test is in Appendix H.

Befonnanm. A nurnbe of performance measures wee collected from the
ﬁnalpracticescerario. Asthisstudyisinteestedintrainingeﬁiciercy,thatis,
learningmoreinanequalamomrtoftime, theﬁnalpracticescerarioisanappropriate
source of the performance data. Participants received 100 points for targets if they
correctly determine the type, class, intent, and engagement Participants lost 100 points
ifatargetpereratedthe outepenalty circle, andSOpoints ifatargetpenetratedthe
irme peralty circle. Measures which indicate the speed, accuracy, prioritization, and
efﬁciency of the participants wee collected by the conrputes and used as pefonmnce
data. Thesemeasmesincludeﬁnalscorenmnbeoftargets engaged, nurnbeof
zooms, nurnbe of speed queies, nurnbe of range queies, mrrnbe of peralty circle
intrusions, and accuracy of ergagenert.

DataAnabzsis
The psychometric properties of the metacognitive activity, learning activity,

79
goal commitmert, and knowledge test scales wee assessed prior to testing the

conceptual model.

The hypotheses in this study wee tested using hiearchical regression
Hiearchical regression can be used to test the mediating hypotheses prOposed in this
study. Todoso, ﬁrstthetwodirectlinksmustbedenonstrated Theindeperdert
variable must have an effect on the mediator, and the mediator must have an eﬂ‘ect on
the deperdert variable. Next, a relationship must be demonstrated betweer the
independent variable to the deperdent variable. Finally, the mediator is erteed ﬁrst
(partialled out) and ther the effect of the indeperderrt variable on the deperdert
variable is reassessed When the mediator is partialled out the relationship betweer
the indeperdert variable and the deperdert variable ought to diminish in a partial
mediation or disappear in a full mediation The regression analyses are outlined in
Table 1. Cognitive ability and gal comnritrnert for the ﬁnal performance gal (the
gal equivalert for both groups and relevant to the deperdert variable) wee erteed in
the ﬁrst step for each regression as control variables.

The ﬁrst regression analysis examined the effects of metacogrritive training and
gal type on metacogrritive activity as measured by the self-report seale (hypotheses 2,
3, and4). Comparisonsbetweerthegaltypesandthepresence/abserce of
metacogrritive training wee contrast coded (Cohen & Coher, 1983). Cognitive ability
wasmteedonﬂreﬁmtstep,ﬂresecondstepcontainedmeacogrﬁﬁveuainingmd
goaltype, arrdthetlrirdtypecontainedthe inteactionbetweergaltypeand

metacogrritive training.

80

The second regression assessed the effects of goal type on learning activity
(hypothesis 1). Cognitive ability and goal comnritment wee erteed on the ﬁrst step,
and gal type was enteed on the second step. Dummy coding for goal type allowed
this regression to examine whethe people with sequerced subgoals performed more
learning activity aﬁe partialling out ability and goal comnritrnert.

Regressions one and two was also run with the post-trial measures of
nretacogrritive activity and learning activity (assuming these ean be reliably coded) as
the deperdert variables.

Regression three assessed the inrpact of metacogrritive activity and learning
activity on knowledge (hypotheses 5 and 6, as well as 8a and 9a). Cognitive ability
and gal commitrnert wee erteed on the ﬁrst step, metacognitive training and gal
type on the second step, and learning activity and metacogrritive activity on the third
step. This analysis examined the effects of metacognitive and learning activity on
knowledge aﬁe cognitive ability, metacogitive training, and goal type have been
controlled

Regression four revesed the orde of entry for steps two and three above.
Thus to test the mediation hypothesis (8a and 93), one expects any effects of
metacogrritive training and gal type to diminish when metacogrritive activity and
learning activity are controlled ﬁrst.

Regression ﬁve tested the hypothesized relation between knowledge and
performance (hypothesis 7). The dependert variable was performance. Cognitive
ability was enteed on the ﬁrst step, and knowledge on the second step.

81
Regression six tested late half of the model (hypotheses 8b and 9b). The

deperdert variable was pefonnance. (Separate regressions will be run for the various
performance measures including score, nurnbe engaged, decision accuracy, and
peralty circle intrusions). Step one of the regression contained cognitive ability and
gal commitrnert Step two contained metacogrritive activity and learning activity.
Step three contained knowledge. In this way the effects of knowledge on per'forrmnce
can be denonstrated afte controlling for metacognitive training, gal type,
metacogitive activity, and learning activity.

Regression seven will analyzed the mediation effects (hypotheses 8b and 9b)
by revesing the orde of entry such that knowledge was erteed ﬁrst, then learning
activity and metacogrritive activity, then metacogrritive training and gal type. The
effects of training, goal type, metacogrritive activity, and learning activity should

dissipate once knowledge was enteed ﬁrst.

82

Table 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Study Hypotheses and Analyses
Hypothesis Independent Variables Dependent Variable Analyses
1 Goal Type (perf. vs. Leanring Activity #2 hiearchieal
sequerced subgals) regression
2 MC training (yes/no) MC Activity #1 hiearchical
regression
3 Goal Type MC Activity #1 hiearchical
regression
4 Goal Type x MC MC Activity #1 hiearchical
training inte. regression
5 Learning activity Knowledge #3 hiearchical
regression
6 MC activity Knowledge #3 hiearchical
regression
7 Knowledge Peforrnarrce #5 hiearchical
regression
8a MC activity, learning Knowledge #3 & #4
activity, MC trarnrn' ' g, hiearchical
Goal type regression
8b MC activity, Learning Peforrnarrce #6 & #7
activity, MC trarmn' ' g, hiearchical
Goal type, knowledge regression
9a MC activity, Leaning Knowledge #3 & #4
activity, MC training, hiearchical
Goal type regression
9b MC activity, [earning Pefonnarrce #6 & #7
activity, MC trarnm' ' g, hiearchieal
Goal type, Inteactiorr, regression
Knowledge

 

RESULTS

D . . B

Table 2 preserts the means, standard deviations, and scale reliabilities for
variables across all subjects. The descriptive indices indicate that the subjects reported
relatively high gal comnritrnert (mean across all three administrations was 6.52 on an
8-point scale). The scales gereally denonstrated suﬂiciert reliability to proceed with
ﬁnthe analyses. (he reliability which was somewhat low was the knowledge test,
which is consistert with the multi-dimersional nature of the test and the one/zeo
scoring (i.e. correct or incorrect). Principle- axis actor analyses indieated that this
seale contained two factors, one comprised of primarily procedural knowledge itens
and one comprised of primarily declarative knowledge items. Descriptives for these
subscales are also provided These scales and the factor analysis will be discussed in
greate detail below.

Subjects, terded to receive fairly low scores (mean = -787) consistert with the
difﬁculty of the task, the high peralty points deducted (mean = 1030), and the fact
that the sirmrlation starts then with a negative score as explained in the Individual
Instructions located in Appendix C. During the ﬁnal trial, participants ergaged an
aveageofalmost7targetsoraboutonepeminute,withanaveageofjustmrdeﬁve

correct decisions.

83

84

The intecorrelatiorrs among variables can be found in Table 3. These zero-
orde correlations indieate that many of the dependert performance variables wee
signiﬁcantly intecorrelated These intecorrelatiorrs reﬂect that the degree to which
participants nrade quick, accurate decisions, protected the cute peralty circle, and
avoided peralty points largely detemine how well they scored consistert with the
desig of the simulation Avoiding inne peralty circle intrusions (and the resulting 50
point peralty) did not correlate signiﬁcantly with score (r = -.14). The independent
variables also showed sonre signiﬁcant intecorrelations. For instance, self-reported
metacogrritive activity correlated with self-reported learning activity r = .46. The gal
cormnitmert measure giver for the successive goals correlated highly across the tinres
.69-.72. The type of gal (learning or performance) did not conelate signiﬁcantly with
reported corrrrrritrrrert at any of the three administrations. Metacognitive training did
not correlate signiﬁcantly with reported metacogitive activity, nor did learning gals
conelate sigrriﬁearrtly with reported leanring activity. This is prelinrinary eviderce
that the nrarripulations did not have a strong effect, or had an urrinterded
consequerces.

To investigate the scales furthe, principle componerts factor analyses wee
peforrned on the nretacogrritive activity seale, the learning activity seale, and the
lmowledge test. These analyses wee pefonned to investigate whethe the scales
containedhnbeddedfaeomwhichcomdbeusedfeﬁnedisﬁneionsmgardingtype
of metacogrritive or learning activity, or speciﬁc types of knowledge. Separate factor

analyses on the metacogrritive and learning activities scales did not yield readily

85
interpretable factors, and eigen values suggested one factor. The learning and
metacogrritive activity scales were theefore left in tact for furthe analyses. The
factor analysis on the knowledge test denonstrated eviderce for two factors.
Following a see analysis and investigation of two and three factor solutions, the two
factor solution eneged as readily intepretable. Consistert with the way the items
wee writter, the knowledge test yielded factors that could be interpreted as primarily
item assessing declarative knowledge and itens assessing procedural knowledge. An
example of an item from the procedural scale (that is, dealing with "how to"
information, usually about prioritizing targets) is, "you have just ergaged three targets
near yom' inne peralty circle, which of the following should you do next..."? The
declarative knowledge iten requires only that the peson real] 3 speciﬁc piece of
information. The declarative seale included itens 2, 4, 6 10 , 11 from the original
knowledge scale. An example of an iten ~ﬁ'om the declarative knowledge seale is "A
submarine nright have which of the following conrrnurrication times..."? The
procedural items gereally involve undestanding a giver situation, and choosing the
best option The procedrn'al knowledge scale was conrprised of iterrrs 1, 8, 17, 18, 19,
20 ﬁrm the original knowledge scale. All othe itens ﬁem the original scale eithe
loaded equally on both factors, or low on both, and were omitted from the analyses
using the declarative and procedural knowledge seales. The alpha reliabilities of the
declarative (or = .63) and procedural (a = .54) scales are close to, or bette than those
of the original knowledge test ((1 = .58).

Anothe issue was potential differential comrrritmert to the learning gal as

86
opposed to the performance goal. To assess differerces, an aveage gal corrmritrnert
scorewasconrputedby aveagingtlrc goal conrrnitrnertscoreacrosstlretlrree
administrations. Thee aveages wee ther conrpared using a t-tests to see whethe the
groups receiving learning gals diﬂ‘eed on aveage from those receiving performance
gals. The mean gal comrnitrrrent aveage for the pefonnance gal subjects was 6.45,
sd = 1.32. The mean for the learning gal subjects was 6.42, sd = 1.20. The
diﬂ‘eerce was not signiﬁcant ( p = .904) indieating that the subjects reported
comparable comnritrnert to eithe type of goal.

 

Hypothesis 1 suggested people giver subgals would ergage in a signiﬁcantly
greate amormt of learning activity than those pursuing pefonrrarrce goals. That is,
people giver subgals sequerced to bring them ﬁ'om declarative knowledge
acquisition, to procedural knowledge acquisition, and ther to scoring wee predicted to
ergage in a signiﬁcantly greate arnormt of learning activity than will those peusing
performance gals. Initial regression analyses did not support this hypothesis. After
controlling for gal cornmitrnert and cognitive ability (which constituted a signiﬁcant
effect due primarily to gal commitment), ertering gal type did not signiﬁcantly
change the R2 for learning activity as assessed by self-report (see Table 4). A
secondary analysis was conducted using a tally of learning activity reported in the post
scerario questionnaire. This analysis was conducted because this tally nriglrt more
accurately reﬂect the amount of learning activity since they are bette able to capture

inter-subject (relative) diffeences. A peson may report that he or she did little, when

87
relative to othe subjects they did a great deal. In addition, the learning activity
questionnaire focuses on diﬂ'eert types of activity and therefore may not eapture the
amount of a particular activity. The tally was conducted by the experimerte afte
coveing the condition nurnbe and shufﬂing the post scerario questionnaires to ersure
that the expeimerte was blind to the condition The tally and learning activity
questiomraire scores correlated only .14 which was not signiﬁeant (p >.10). Typieal
learning activities wee repetition of the cues, trying to detemine an undelying logic,
devisingasysternformemorizingthe cues, andtryingtodetermineabstractmles
urrdelying the simulation The regression analysis using this tally as the deperdert
variable supported Hypothesis 1. Aﬂe controlling for cognitive ability and gal
corrrrrritrnert, gal type signiﬁcantly impacted the amount of learning activity reported
in the post-scerario questiormaire (AR2 = .09, p = .001, see Table 5). That is, subjects
reponedmorelemrﬁngactivityonﬂrepostscermioquesﬁomairewherﬂreywee
giver sequerced subgoals than wher they wee given performance gals.
I l . . § . . l I . .

Hypothesis 2 suggested that Metacognitive training would be positively related
to metacognitive activity performed in support of learning. That is, individuals giver
metacogitive training would report and demonstrate increased use of nretacognitive
activity to assess and optimize their learning Hypothesis 3 suggested that sequerced
subgals would lead to more metacognitive activity in support of learning than
pefonnance gals. In addition, Hypothesis 4 suggested that rrretacogrritive training and
goal type would inteact to detemine the amount of metacogrritive activity devoted to

88

learning. Those with sequerced learning subgoals wee predicted to ergage in more
metacogrritive activity as a result of metacognitive training. These hypothesis wee
tested in the hiearchical regression depicted in Table 6. The deperdert variable was
self-reported metacogitive activity from the metacogrritive activity scale. Aﬂe
controlling for cognitive ability on step one, step two contained drnrrrny variables for
metacogrritive training and learning gals, and step three contained their inteaction.
Neithe step added signiﬁcantly to the variance accounted for in self-reported
metacogrritive activity. (The AR2 for step two was .002, and .000 for the inteaction
on step three.) Thus, Hypotheses 2, 3, and 4 wee not supported

To follow up this analysis, two rates blind to the subjects' expeimertal
condition rated the quality of metacogrritive activity reported in the post-scerario
questionnaires. Rates wee asked to evaluate the questionnaires for metacogitive
activityonasealeof1(lowest)to 5 (highest). Ratesmetwiththeexpeimerteto
clarify the deﬁnition of metacogrritive activity, and ther rated discussed 10
questionnaireresponsestocalibratetlreirratings. Ther, tlreyratedalloftherenaining
100 questionnaires independertly. Their ratings correlated .72 signiﬁcantly (p < .01),
onlytwiceweedisagreemertsbymoretlrarr 1 pointandnevemorethaanoints.
Theratingsweetheraveagedandthiswasusedasaratingofthequdity of
nretacogrritive activity reported in the post scerario questionnaires. These ratings wee
completed to determine if thee was beta change in the subjects' self-report. That is,
pilot testing indieated that those with metacogitive training may hold tlrenselves to a

highe standard wher judging their own metacognitive activity. The post-scenario

89
questionnaire was an oper ended question regarding how they attempted to meet their
gal. The ratings correlated with the self-reported metacognition r = .25 (p < .01).
The sarrre hiearchical regression pefonned above was repeated to test Hypotheses 2,
3, and 4 using the ratings as the deperdent variable.

The analyses wee similar to those reported earlie. The only variables that
contributed signiﬁcantly to the metacognitive ratings wee the controlled variables,
cognitive ability and gal conrrnitrnert Neitlre the learning gals, the metacogrritive
training, nor the inteaction had a signiﬁcant regression weight Likewise, the change
in R2 was not signiﬁeant for the second and third steps (see Table 7).

A ﬁnal analysis was run using a tally ofthe amount ofmetacogrritiorr as
reportedindrepostscerarioquestiomraires. Thiswasanattempttomeasurethe
amountofmetacognition subjects ergagedinduringtlretask-toquarrtify the
amount of metacogrritive activity reportedby the subject on the post-scerario
questionnaire. Thesrnmnarymeasmewasconductedinthesamemarmeasthe
learning activity tally. The expeimerte counted the nurnbe of times nretacogrritive
activity was indieated by subjects on the post scerario questiomraire. Aﬁe controlling
for cognitive ability, the step containing learning gals and metacogrritive training
contributed signiﬁcantly with a change in R2 = .08, due primarily to the
metacogitive training (B = .38, p < .01), the regression weight for the gal type was
not signiﬁeant (B = .10, p > .05). The inteaction was also not signiﬁcant This
analysis (outlined in Table 8) indicated some support for Hypothesis 2, that

metacogrritive training would lead to nrore metacogrritive activity.

90

Hypothesis 5 suggested that increased learning activity would lead to increased
learning in terns of declarative and procedural knowledge. Similarly, Hypothesis 6
suggested that metacogrritive activity would be positively related to knowledge.
People engaging in more metacogrritive activity will denonstrate increased declarative
and procedural knowledge. These hypotheses wee tested by regressing learning and
nretacognitive activity onto knowledge as assessed by the declarative and procedural
seales of the knoWledge test For these regression analyses, cognitive ability and gal
cornrnitrnert wee ertered at the ﬁrst step, metacogrritive and learning activity wee
erteed for the second step. All of the diffeert measuremert techniques (the scales,
ratings, andtallies)weeerteedonthisstepsinceallweemeasuresofthesarne
variables without a strong a priori theory as to which would be the most efﬁcacious
technique, thus the regression weights may help to indicate which wee the strongest
meosurenert techniques for deternirring the impact of metacognition on knowledge
acquisition. This regression was repeated for the declarative and procedural knowledge
subscales as the dependert variable (see Tables 9-10). The results indieated that
cognitive ability was related to knowledge, but metacogrritive activity and learning
activity generally did not add signiﬁcantly in terns of ARZ. It is, perhaps, noteworthy
that the ratings of nretacogrritive activity had the strongest regression weights of the
methods of metacognition measurenert, approaching signiﬁcance for the procedural
knowledge scale. Oveall, howeve, these analyses did not support Hypotheses 5 or 6.

Knorulelaandﬂerfonnance
Hypothesis 7 suggested that knowledge would be positively related to skill

91
performance. Individuals demonstrating hi ghe levels of declarative and procedural

knowledge wee expected to exhibit bette performance. Seveal variables wee
collected to assess performance. These include the oveall score, the nurnbe of
targets ergaged (indieates speed), correct decisions (accuracy of ergagemerts), peralty
points assessed, inne peralty circle intrusions, and cute peralty circle intrusions. The
analyses wee performed by ertering gal commitrnert and cognitive ability on step
one, and ther declarative and procedural knowledge on step two (see Tables 11
through 15) . The step containing declarative and procedural knowledge was
signiﬁcant for score (AR2 = .17, Bd=.31, Bp=.32), correct decisions (AR2 = .16, [35:34,
Bp=.28), and cute pemlty circle intrusions (AR2 = .06, Bd= -.07, Bp= -.25). Only the
regression weight for procedural knowledge was signiﬁcant in the regression for cute
peralty circle intrusions. For the nurnbe ergaged, the change in R2 was not
signiﬁcant for the knowledge step (p = .13), but the regression weight for declarative
knowledge approached signiﬁcance (p = .07). Similarly, for peralty points - the
change in R2 was not signiﬁcant (p = .14), but the procedural knowledge regression
weight approached signiﬁeance (p = .06). For inne peralty circle intrusions, neithe
the change in R2, nor the regression weight ever approached signiﬁeance. Oveall,
these analyses support Hypothesis 7, that declarative and procedural knowledge are
related to highe performance.
Wanna.

Hypothesis 8a suggested that the effects of metacogrritive training on
lmowledge acquisition would be fully mediated by the impact of this training on

92
metacogrritive activity. Metacognitive training was not expected to exhibit direct
effects on knowledge acquisition To examine mediation, one must denonstrate
relationships from the ﬁrst variable to the second (a to b), ﬁom the second to the third
(b to c), and the relationship between the ﬁrst and third (a to c) must diminish wher
tlresecondisparitalledoutﬁrst Theeislimitedevidercethattheﬁrstvariable
(nretacogrritive training) impacted the second (metacogrritive activity). The training
did impact the amomrt of metacognition reported in the post-scerario questiomraire
(indicatedbythetallyofsuchstatenerts), arrdsotheeissorneeviderceofthe“ato
b” link Howeve, metacognitive activity did not impact the declarative or procedural
knowledgescores, sothe“btoc”linkismissing. Theeforeﬂreeeanbenotestfor
mediation, and Hypothesis 8a is not supported (The regressions demonstrating this are
loeated in Apperdix M).

Hypothesis 8b suggested that the effects of metacogrritive training on skill
pe'formance will be ﬁrlly mediated through effects of this training on metacogrritive
activity, and metacogrritive activity on knowledge developmert or performance. As
before, this hypothesis was tested by hierarchical regression on the pefornrance
variables, ther reversing the orde of entry. In gereal, the mediational hypotheses
received only mixed support Knowledge tended to have the strongest effect,
regardless of the orde of ertry. Howeve, for peralty points and nurnbe of targets
ergaged the eﬁ‘ect of knowledge did dissipate wher metacogitive activity and training
wee erteed ﬁrst (see Tables 17 and 18). Howeve, the effects for metacogrritive

training and activity themselves did not appear to inﬂuerce the deperdert variables in

93
the these eases, meaning there is a lack of the “b to c” linkage as well as the

aforenertioned “a to b” linkages. Theefore, thee can be no test for mediation, and
Hypothesis 8b is not supported (The regressions demonstrating this are located in
Apperdix N.)

Hypothesis 9a suggested that the effects of gal type on knowledge acquisition
would be mediated by the effect of goal type on the learning and metacogrritive
activity. Goal type was not expected to exhibit direct eﬂ"ects on knowledge acquisition
As above, this hypothesis was tested using hiearchical regression with revesal of
ordetodetemineiftheeweeeﬁects oflearrring gals onpeforrnarrce, andifthis
relationship diminished wher learning activity is added ﬁrst . Howeve, the
relationship betweer gal type and declarative and procedural knowledge wee not
signiﬁcant aﬂe controlling for cognitive ability and gal comrrritrnert Theefore,
thee was no relationship to mediate. The regression weights for learning activity
tended to be negative as well. These results do not support hypothesis 9a.
(Nevetlreless, the regression analyses can be found in Appendix 0.)

Hypothesis 9b, stated that the effects of goal type on skill performance would
be ﬁrlly mediated through effects of goal type on learning activity, metacogrritive
activity, their effects on knowledge developmert Goal type was not expected to
exhibit a direct effect on knowledge acquisition or pefonnance. These hypotheses
wee tested as above, using hiearchical regression, and revesal in the orde of ertry.
As above, howeve, thee was limited eviderce to support the necessary linkages, in
particular there was little evidence that goal type had an effect on performance

94
variables to be mediated Oveall, only knowledge had consistent effects across

pefonnance variables, and these effects wee not mediated substantially by learning
activities or goal type. For example knowledge accounted for signiﬁcant variance in
score, correct decisions, and oute peralty circle intrusions (procedural). These effects
wee maintained regardless of orde of entry. The metacogrritive mesmes showed
some signiﬁeant regression weights, but did not impact variance accounted for
signiﬁcantly. Of these signiﬁcant regression weights, two wee for metacogrritive
activity measured by tallying up metacogrritive statenerts in the post scerario
questiormaire, but these wee negative weights. That is, metacogrritive activity was
negatively related to targets engaged and correct decisions made. Hypothesis 9b was
theefore not supported (Analyses are located in Apperdix P.)

95
Table 2

Means, SDs, and Reliabilities of Study Variables

 

VARIABLE # ITEMS MEAN SD or
Metacognitive Activity Score 13 4.42 .88 .72
Metacognitive Activity Rating 2* 2.37 .82 -
Metacognitive Activity Tally — 1.64 1.46 —
learning Activity Score 17 3.69 .80 .81
Learning Activity Tally — .89 .89 -
Goal Conrnritmert 1 3 6.45 1.24 .96
Goal Commitment 2 3 6.37 1.33 .97
Goal Commitrnmt 3 3 6.77 1.31 .98
Knowledge Test 20 11.92 2.78 .58
Procedural 6 4.66 1.30 .54
Declarative 6 2.54 1.62 .63
3001? - -787 374 -
Targets Engaged — 6.97 2.60 —
Correct Decisions — 4.70 2.32 -
Penalty Points (deducted) - 1030 95 -
Outer PC Intrusions - 9.42 .88 -
Irrrre PC Intrusions - 1.75 .79 —

 

*nurnbe of rates

96
Table 3

Intecorrelations Among Study Variables

 

VARIABLE 1 2 3 4 5 6 7 8 9 10 11
1. Score -

2. NumberFrrgaged .28* —

3. CorrectDecisions .86* .71* —

4. PenaltyPoints -.51* -.38* -.43* -

5. OutePCIntrusions -.48* -.26"' -.35"‘ -.98"' —

6. InnePCIntrusions -.14 -.34* -.23* .36* -.06 -

7. learning Activity” .04 .01 .02 -.08 -.05 -.08 -

8. MCActivity" .17 .13 .16 -.23* -.16 -.19* .46* —

9. Goal Corrrrrr. 1 .20* .21" .25" -.12 -.15 .05 .31“ .41" —

10. Goal Conrrrr. 2 .20“ .22* .26* -.10 -.13 .04 .32" .42“ .72" --
11.GoalConrrn3 .09 .07 .ll -,05 -.10 .10 .32 .37“ .69* .72“ -
12.l.earningGoal‘ -.15 .11 -.04 .06 .08 -.02 .09 -.02 .02 -.02 .14
13. MCTraining' -.15 -.15 -.19 .01 .04 .16 .01 -.06 -.02 .04 .04
14. Knowledge Test .42* .27* .43* -.29* -.33* .04 .04 .19" .24“ .17 .11
15. Declarative .34* .22“ .37* -.13 -.12 -.04 .07 .11 .18 .16 .10
16. Procedural .29“ .15 .27* -.22"‘ -.28* .11 .02 .11 .19* .10 .07
17. MCRatings .26* .12 .25“ -.ll -.09 -.06 .19“ .25" .29“ .26" .18
18.MCTally .02 -.ll -.07 -.l4 -.14 -.03 .11 .16 .10 .17 .15

19. LearningTally -.02 .04 .02 .07 .07 .02 .14 .02 -.08 -.01 .03

 

n=106, " p < .05, a=coded, 1= learning goal/MC Training; 0 = perf. goaVno MC Training,
b=self-report score

97
Table 3 (Cont'd)

Intecorrelations Among Study Variables
VARIABLE 12 13 14 15 16 17 18 19

 

12. learning Goals‘ -
13. MC Training' -.03 -
14. Knowledge Test -.02 -.06 —

15. Declarative .10 -.13 .62" -

16. Procedural -.1 1 -.03 .66* .07 -

17. MCRatings .02 -.12 .30“ .16 .26* -

18. MC Tally .01 26* .12 -.01 .08 .47* -

19.1.eamingTally 21*-.09 -.11 -.ll -.09 .13 .11 .—

 

n=106, "‘ p < .05, a=coded,1=learning goal/MC Training; 0 =perf. goal/no MC Training, b=self-leport
score

98
Table 4

Hiearchical Regression Results of the Test of Hypothesis 1- Impact of Goal Type on
Learning Activity as indicated by the Learning Activity Questionnaire

 

 

VARIABLE B AR2 R2
STEP 1:

Goal Commitmert .36" .13 .13**
Cognitive Ability -.04

STEP 2:

Goal Type .07 .01 .14**
*p<.05, **p<01

n=106

99
Table 5

Hiearchical Regression Results of the Test of Hypothesis 1- Impact of Goal Type on
Learning Activity as indicated by the Post Scerario Questionnaire

 

 

VARIABLE [3 AR2 R2
STEP 1:
Goal Comnritrnent -.01 .004 .00
Cognitive Ability -.06
STEP 2:

. Goal Type .30 .09" .09*
‘p<05, **p<01

n=106

100
Table 6

Hiearchical Regression Results ofthe Test onypotheses 2, 3, and 4 — Impact of
Metacognitive Training, Learning Goals and their Irrteaction on Self Reported

 

 

Metacognitive Activity
VARIABLE [3 AR2 R2
STEP 1:
Cognitive Ability -.02 .202” .20"
Goal Conrrnitrnert .45
STEP 2:
MC. Training -.07 .006 .21"
Goal Type -.04
STEP 3:
MC. Training X .00 .000 .21“
Goal Type
‘p<05, **p<01

n=106

101
Table 7

Hiearchieal Regression Results of the Test of Hypotheses 2, 3, and 4 — Impact of
Metacognitive Training, learning Goals and their Interaction on Rated Metacognitive

 

 

Activity
VARIABLE B AR2 R2
STEP 1:
Cognitive Ability .21“ .12** .12**
Goal Commitmert .23M
STEP 2:
MC. Training -.08 .010 .13"
Goal Type .03
STEP 3:
MC. Training X -.04 .000 .13“
Goal Type
*p<05, "p<.01

n=106

102
Table 8

Hiearchical Regression Results of the Test of Hypotheses 2, 3, and 4 - Impact of
Metacognitive Training, Learning Goals and their Inteaction on the Tally of

 

 

Metacognitive Activity
VARIABLE B AR2 R2
STEP 1:
Cognitive Ability .12 .030 .03
Goal Conrrnitrnert .12
STEP 2:
MC. Training .38" .07* .10*
learning Goals .00
STEP 3:
MC. Training X -.18 .01 .11*
learning Goal
*p<05, **p<.01

n=106

103
Table 9

Hiearchieal Regression Results of the Test of Hypotheses 5 and 6 — Impact of
learning and Metacognitive Activity on Declarative Knowledge

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability .29" .121** .12**
Goal Commitment .06

STEP 2:

learning Activity (tally) -.09 .021 .14**
MC. Activity (tally) -.10

learning Activity (score) .02
MC. Activity (ratings) .11
MC. Activity (score) .04

 

*p<.05, **p<01
n = 106

104
Table 10

Hiearchieal Regression Results of the Test of Hypotheses 5 and 6 — Impact of
learning and Metacognitive Activity on Procedural Knowledge

 

VARIABLE [3 AR2 R2
STEP 1:

Cognitive Ability .29** .121** .14**
Goal Conrrrritrrrert .06

STEP 2:

Learning Activity (tally) -.09 .021 .18"
MC. Activity (tally) -.10

learning Activity (score) .02
MC. Activity (ratings) .11
MC. Activity (score) .04

 

*p<.05, **p<Ol

105

 

 

Table 11
Hiearchical Regression Results of ﬁre Test of Hypotheses 7 — Inrpact of Declarative

and Procedural Knowledge on Score
VARIABLE B AR2 R2
STEP 1:
Cognitive Ability -.03 .064* .06*
Goal Commitmert .08
STEP 2:
Declarative .31** .166** .23"
Procedural .32**
*p<05, **p<01

n=106

106
Table 12

Hiearchieal Regression Results of the Test of Hypotheses 7 — Impact of Declarative
and Procedural Knowledge Nurnbe of Targets Engaged

 

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability -.03 .042 .04
Goal Comnritrnert .08

STEP 2:

Declarative .1 1 .037 .08
Procedural .18

*p<05, ”p<01

n=106

107
Table 13

Hiearchical Regression Results of the Test of Hypotheses 7 - Impact of Declarative
and Procedural Knowledge on the Nmnbe of Correct Decisions Made

 

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability -.03 .081 .08
Goal Comnritmert .13

STEP 2:

Declarative .34“ .157** .23"
Procedural .28"

*p<05, **p<01

n=106

108
Table 14

Hiearchieal Regression Results of the Test of Hypotheses 7 - Irrrpact of Declarative
and Procedural Knowledge on the Nurnbe of Penalty Points Deducted

 

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability -.05 .034 .03
Goal Conrrnitrnert -.04

STEP 2:

Declarative -.09 .037 .07
Procedural -.19

*p<05, **p<01

n=106

109
Table 15

Hiearchieal Regression Results of the Test of Hypotheses 7 — Impact of Declarative
and Procedural Knowledge on the Nurnbe of Date Penalty Circle Intrusions Allowed

 

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability -.06 .046 .05
Goal Commitmert -.07

STEP 2:

Declarative -.07 .06* .11"'
Procedural -.25**

*p<05, **p<01

n=106

110
Table 16

Hierarchieal Regression Results of the Test of Hypotheses 7 - Impact of Declarative
and Procedural Knowledge on the Nurnbe of Inne Penalty Circle Intrusions Allowed

 

 

VARIABLE B AR2 R2
STEP 1:

Cognitive Ability .01 .006 .01
Goal Comnritrnent .07

STEP 2:

Declarative -.06 .015 .021
Procedural .12

*p<05, *‘p<01

n=106

111

Table 17

Hiearchical Regression Results of the Test of Hypotheses 8b - The mediation of the
effects of Metacognitive Training and Activity on Nurnbe Engaged Through

 

 

Knowledge
VARIABLE B AR2 R2
EQUATIQIrLl
STEP 1:
Cognitive Ability .00 .042 .04
Goal Corrrrnitrnent .12
STEP 2:
Declarative .16" .037 .08
Procedural .09"
STEP 3:
MC. Activity (tally) -.17 .034 .11
MC. Activity (score) .04
MC. Activity (rating) .10
STEP 4:
MC. Training -.07 .005 .12
W2
STEP 1:
Cognitive Ability .00 .042 .04
Goal Commitmert .12
STEP 2:
MC. Training -.08 .021 .06*
STEP 3:
MC. Activity (tally) -.18 .029 .09*
MC. Activity (score) .04
MC. Activity (rating) .10
STEP 4:
Declarative .16 .026 .12
Procedural .09
*p<05, **p<.01

n=106

112

Table 18

Hiearchical Regression Results of the Test of Hypotheses 8b — The mediation of the .
effects of Metacognitive Training and Activity on Penalty Points Through Knowledge

 

 

VARIABLE B AR2 R2
W

STEP 1:

Cognitive Ability -.05 .030 .03
Goal Commitmert .05

STEP 2:

Declarative -.09** .037 .04
Procedural -.19

STEP 3:

MC. Activity (tally) -.18 .053 .12
MC. Activity (score) -.21

MC. Activity (rating) .11

STEP 4:

MC. Training -.13 .014 .13
W

STEP 1:

Cognitive Ability -.05 .034 .03
Goal Commitmert .05

STEP 2:

MC. Training .13 .008 .04
STEP 3:

MC. Activity (tally) -.18 .061 .100
MC. Activity (score) -.20

MC. Activity (rating) .11

STEP 4:

Declarative -.08 .034 .13
Procedural -.19

*p<05, **p<01

n=106

DISCUSSION

This study attempted to eeate an efﬁcient method of training using the
implications of the Kanfe and Ackeman (1989) limited resource model of skill
acquisition A ﬁarnework was developed, hypotheses proposed, and this model was
tested using a complex radar simulation task The hypotheses and theory suggested
that since learnes' resources are primarily devoted to learning declarative information
early in skill acquisition, and procedural infomration at an internediate stage, more
eﬂiciert learning could result ﬁom setting learning gals to direct resources to
learning activity. This leanring activity would help acconrplish to the "subtasks" of
attaining declarative and procedural knowledge. Likewise, it was proposed that the
metacogrritive processes which guide the devotion of resources and use of strategy
could be improved with training leading to more effective use of resources, bette
learning of declarative and procedural knowledge, bette strategy selection, and
ultinrately pefonnance. Results gererally did not support the model. Instead, it
appeared that cognitive ability (an indicator of trait resources the leame brings to
bear) related to knowledge acquisition, and knowledge acquisition related to bette
performance. The manipulations of goal type and metacogrritive training had little
effect on the amount or quality of the learning activity and metacognition pefonned
by the subjects after controlling for the effects of goal commitrnert and cognitive

ability. In addition, the amount of learning activity and metacognitive activity had

113

1 14

linrited effects on the acquisition of knowledge and subsequert pefonnance.
Wacognition

Orreoftherrrajormanipulations ofthisstudyWastheattempttotrain
metacognition, a critical support function to knowledge acquisition and pefonnance.
Howeve, this rmnipulation was not successﬁrl at increasing the amount or quality of
metacognition asmeasuredintlrreeseparateways. Thetrainingusedhadbeer
subjected to two pilot tests and iteatively strergthered prior to this study, but was still
gereally unsuccessful at increasing or improving metacognition. One reason this
manipulation may have failed was that metacognition is a skill developed ove a
lifetinreoflearningandistoodiﬂiculttotraininoneshort, irrtersesessiorr. Past
studiesthathavebeermoresuccessful atinrprovingmetacogrritionhaveterdedto
occur in educatioml settings, and have been long in both duration (sessions ove time)
arrdstudytime. Incomparison, thecmrertstudyattemptedtotrainmetacognitionin
one irrterse “massed” session prior to ergaging the task Giver that metacognition is a
sldlLresemehhasdenomuatedﬂratsldﬂseelemnedbeuemspacedpracﬁceﬂran
massed

In addition, the present study was handicapped by the necessity ofmaking sure
the nretacogrritive training group gt no extra time on task, information about the task,
or exposure to task information. To meet these conditions, the training was peformed
on nrateial sinrilar to, but not the same as, the actual infomration used on the task
This indirect training may have decreased transfe to some extert Moreove, the

eviderce in this study suggests that training metacognition in one intense session may

115

not be the most efﬁcacious way to change metacognition wher a long tinre peiod is
not available to devote to metacognitive training.

Metacognitive activity was not found to be particularly effective at explaining
variance in knowledge acquisition or perfonrrance. Three methods wee used to try and
eaptrrre metacognition One method, ratings, seened to be more efﬁcacious in
capturing the aspects of metacognition which does improve learning and performance.
The three mehods did correlate with one anothe (r =.25, r = .16, r = .47), with the
highest correlation being between the tally of metacognition ﬁom the post-scerario
questionnaire and the ratings of the metacognition in the post—scerario questionnaire.
This is logical in that someone who did very little metacognition likely did not do
erough to meit high ratings. The regression weights for the diffeert methods
indicatethatofﬂreﬂrreetypesofmeasmenerttheratingsby indeperdertraters
showed the most promise in eapturing metacognition that improves learning and
performance. The self-report and tally methods captrned primarily the anount of
metacognition - eithe how much of each metacognitive activity ﬁom the
metacogrritive questionnaire or how much oveall ﬁom the tally. The ratings, on the
othe hand, may have been less deﬁcient in that they were focused on capturing the
quality of metacognition, not meely the amormt That is, a peson could lmve said “I
am leaning the cues better” seveal times on the post-scerario questionnaire (as some
subjects did) which boosts the metacogrritive tally and indeed reﬂects an assessment of
leaning which would show up in a self-report Howeve, it is not particularly

diagnostic or leaning, nor helpful for strategy formation. Good metacognition

116

involves accurate self-awareress, diagnosis, and prescription of a good renedy to the
problens oneishaving. Thisappeastolmvebeereapturedbetteintheratingsthan
othe measurenrert techniques. The regression weights for the ratings wee typically
high and in the predicted direction relative to othe metacognitive measuremert
techniques on seveal measured criteria (e. g. declarative knowledge, procedural
knowledge, score, nurnbe ergaged, correct decisions). The othe methods sometimes
had high relative weights, but otter in the opposite direction as predicted (e. g. correct
decisions andnurnbeergaged). Thismaybeanindicatorthatthe qualityarrdnottlre
amount of metacogrritive activity is a bette way to opeationalize metacognitive
activity. Thismaybeespeciallytrueforanexpeimertalsettingwheeamomrt may
morereadily indieatedenandetfectsthanqualitymeasmes. Thatis,subjectsmaypay
more “lip sevice” to metacognitive activities due to the training, but it is the quality
of the activities that demonstrates the state of the peson’s metacognition and will
improve leaning and perfonmnce. Quality ratings appear to hold the most promise as
ameasmemeﬁtechrﬁqueatassessingﬂreaspeeofmeacogrdtionﬂratudﬂbe
bereﬁcial to pefonnance.
Lemingiicah

The leaning gals related to leaning activity in the tally of statenerts ﬁom
the post-scerario questionnaire, but not the leaning activity questionnaire itself. This
seenstoindieatethatalthoughtheleaninggal groupdidfocusonleanirrgasanerd
irritselﬂtheydidnotadoptastrategythatwasdiﬁeertﬂrmﬂrebdravieﬂre
pefonnance gal group was doing by default This may be a function of the task

117

The task involves learning cue values and prioritizing targets to prevert then from
erteing peralty zones. Although many possible leaning strategies exist, and leanring
gal subjects wee told not to focus on their score (and ever had the score feedback
turnedoﬂ),tlremostobviouswaytoleanthecuesarrdtlregarnewassirnplytog
through the targets. The researches had anticipated that subjects would execise the
option of spending extra time studying the mateials, creating nmemorrics, visualizing,
relating infomration or using othe strategies in combination Howeve, the task with
its repetitious mture seems to inspire a repetition strategy. Most people, regardless of
gal condition, learned the cues by ging through the targes and looking at the review
sheet less and less. Ever if this was not the most efﬁcient strategy, it was the strategy
virtually all subjects adopted So it would appea' that the repetition strategy adopted
bytlrelearringgal groupwasthesamebehaviorergagedinbytheperformancegal
subjects. Thus, they did not change much or bereﬁt especially from the leaning gal
manipulation and looking at learning as an erd in itself early in the task

The procedural manipulation also did not have much impact on leaning
activityorperformance. Thiswasaﬁrstattemptatdevelopingaprocedrnal leaning
gal which could both adlree to the deﬁnition of procedural lorowledge, remain
speciﬁc, and be urrdestood by the subjects. Subjects in the leaning goal condition
wee ercouraged to use the task to develop god arrswes to complete the second half
of “if/ther” statenrerts which wee an attempt to capture the if-then nature of
procedural knowledge. Prioritizing targets was one of the most important task
ﬁmctions using procedural knowledge. Most subjects did vey little in tems of

118
learning activity, hypothesis testing, purposeful exploration, or othe means of leaning
procedural knowledge (othe than repetition) to respond to the situations described in
the “if” statement they wee giver. They seemed to simply repeat task instructions
rathetharrthinkingdeeply aboutthenatureoftlretask Or, theynrayhavebeen
thinking about the task, but without having intenalized erough of the declarative
lorowledge to lead to effective procedures (i.e. the procedural manipulation came too
soon). The rules they gereated wee otter poor strategically. For instance, many
indicated that they would guard the inne penalty circle more than the oute circle,
ever though the inne circle was worth fewe points. As with the metacognitive
activity, simply devoting attertion to procedures does not improve one’s leaning
autonratically. Ratlre, thee has to be quality resources devoted, and some logieal
thought used based on accurate knowledge.

Neitlre leaning activity nor metacognition related to substantially bette
leaning or perforrmnce. One possibility for both leaning and rrretacogrritive activity
isthatlearringarrdperformarrcewee irnprovedblrtweenotcapturedduetothe
timingofmeasurement Thisstudywasconcenedwitherdoftrainingpeformance
and looked at peformance only at the erd of alnrost 50 minutes of task exposure. It
may be that people who ergaged in theses behaviors learned the infomration faste
(i.e. by the second or third trial) but not bette. Eventually, by the erd of the ﬁfth
trial wher the knowledge test was presented, the othe subjects may leave caught up.
Likewise, perfonnance may have had some initial irnprovernerts, but by the sixth and
ﬁnal trial (during which performance measures wee collected) any initial margins had

119
diminished An examination ofthe performance mapped ove time could denonstrate
any initial gains.

Of course while measming too late nriglrt be one problem, anothe timing
problen could have been that the subjects with leaning gals needed more time. In
the short run, the gals may have nrade little diffeence, but giver longe tinre on task,
with nrore spacing betweer leanring goals, they may have beer more eﬁective. Otlre
reseaehes (Kanfe and Ackerman for exanrple) are able to have subjects work on the
taskformanymorehomsthansubjectsinthepresentstudy. Itmaybethatthe
learning gals moved too quickly to the procedural stage, wher really the gals would
havebeenmorebereﬁcial ifspacedbette. Thatis, subjectsrrrayhaveneedednrore
time before they wee ready to move on to procedualization issues. A longe
assessmertusingrepeaedmeasmeswouldbenecessaytodeectsuchpheromera
Knorrlm

Knowledge was one of the most powerful predictors of performance in the
model. The pattern of correlations indicates that declarative knowledge was more
related to speed/accuracy aspects of pefonnance, and procedural knowledge was more
related to strategic aspects of pefonnance like prioritimtion For exarrrple,
correlations betweer declarative knowledge and the nurnbe of targets ergaged is
signiﬁcant, but procedural knowledge and nurnbe ergaged was not signiﬁcant On
the othe lurnd, procedural knowledge related signiﬁeartly to peralty points and oute
penalty circle intrusions (indicators of prioritization) wheeas declarative knowledge

did not. Similar patters were demonstrated in the regressions. The regression weights

120
for declarative knowledge are hi ghe than those for procedural knowledge for nurnbe

of targets ergaged and correct decisions (speed and accuracy indicators). Meanwhile,
the regression weight for procedural, but not declarative knowledge is signiﬁcant in
predicting oute peralty circle intrusions which reﬂect the prioritization of the targets.
Both correlate signiﬁeartly and have signiﬁcant regression weights with score,
consistert with the task design in that both speed, accuracy, and strategic prioritimtion
wee important to pefonnance.
Thisdiscussionhascerteedaromrdthepossibilitythattheresults ofthisstudy
rmy have beer a function of limitations in the methodology. Anothe alterrative is
that the theoretical fornrdations upon which the presert model was built are
problematic. The results obtained can be construed as consistert with the linrited
resource model in that this model predicts that early in skill aquisition, self-regulation
will orrlybebereﬁcial ifitresults inanetirrcreaseinresoucres. Thatis, ifthe self-
regulationsomehowﬁeesmoreresomcesthanitconsmnes. Onecarrthussaytlrattlre
currert results mean that metacognition was not particulary powe'ful at increasing
knowledge and performance because it did not yield a net increase in on-task
resources. Howeve, this leads to question of how one can know whethe one has
causedanetineeaseinresomces? Howeanonemeasurethenetresourcegainsand
losses? It is rathe circular to suggest that any regulation which leads to better
pefonnance increased net resources, and any which lead to worse pefonnance

decreased net resornces. A pefonnance increase may be caused by self-regulation

121
which decreases net resources but yields a bette strategy for using the fewe resources

which remin Likewise, poorer pefonnance might be the result of self-regulation
whichincreasesnetresources, butdecresestheetﬁcacy oftlrewaytlreyareused
Again,thelimitedresomcesmodel suggeststhatamee increaseofresourcesleadsto
an increase in pefonnance. This is not always the case. The way in which these
resourcesareusedmaybeakeytoperformancevariance-Mopeoplebothhighin
resources coulddenonstratevey diﬂ‘eertpefonnanceonthetaskusedforthis study
using diffeert strategies.

This highlights anothe problematic distinction for Kanfe and Ackeman.
They distinguish betweer on-task and oﬁ-task resources. Although some uses of
resources can reasonably be considered off-task (e. g. thinking about lrnrch instead of
the task), othe activities are consideed oﬂ°-task by Kanfe and Ackeman which may
beveyimportarrttothe longtermsuccessoftlretask Onemayﬁndthatself-
regulatory thought early in skill acquisition is detrimental to pefonnance at that time,
but this regulation may erqredite increases in pefonnance ove time. Using the ﬁrst
trialtestoutstrategiesisapooruseofresourcesintemsofﬁrsttrial success,butitis
critical to success in othe trials. Is this self-regulatory thought ther consideed oﬁL
task even though it is about the task and leads to bette pefonnance? The failure to
discuss what the criteia are for an activity to be consideed on-task or off-task, mixed
with the failure to distinguish leaning ﬁom performance gives one little guidance
about what activities to ercourage for those aquiring a skill.

Anothe, pehaps more fundamertal, theoretical consideration is whethe self-

122

regulation is resources consrmrptive. The Kanfer and Ackeman model posits that
proximal motivation (i.e. self-regulation) is resources consumptive which is the critical
assunrption for explaining pefonnance decremerts due to competing resources.
Howeve, othe reseaches suggest that self-regulation is an automatic process and
requires little, if any, resources. Lord and Levy (1994) suggest that monitoring
pefonnance to detect gal discrepancies (or what Kanfe and Ackerman call self-
morritoring and self-evaluation) is data driver and automatic. Similarly, Bargh (1989)
reviewed the enpirical lituratrrre which denonstrated that situational scripts and
complex action squerces (gereal memory structures) automatieally guide attertion
and behavior wher working toward a conscious gal. Since these scripts are
genealizeable across problens, they are applied repeatedly, ﬂexibly, and automatically
to new situations. This reseach would suggest that self-regulation effects performance
notby increasingordecreasingresom'ces devotedtothetask, butbyalteringthe
behaviors and strategies selecting those which are more effective at reducing gal
discrepancies. Thus, those who employ more effective automatic processes will
choose bette strategies than those with poore self-regulation

The ﬁndings in the currert study are consistert with the notion of automatic
regulation Participants may have (and in fact appeared to have) applied leaning
techniques which wee familiar, whethe or not they wee effective and regardless of a
manipulation urging then to actively conside and evaluate their strategy. They may
have been guided by the kind of , automatic process Bargh considers. This is especially
trueforadult collegestudentssuchastheparticipants intlrisstudy. Marryyears of

123

school has likely created strong scripts and action sequerces to guide their leaning
behavior with little conscious thought. It may be that given a particular type of
leaning task, a particular script formed ove many yeas is activated For exarrrple,
giver a task requiring memorization, participants would automatically begin using a
relresal strategy which is familiar,one they have used in the past. This may take place
with little conscious consideation of the strategy enployed, or possible altenative
strategies which may be more effective.

If in fact self-regulation is to sorrre exert automatic, then one would expect that
a one time manipulation aimed at consciously changing regulation would have limited
succes. Itnraynotworkatall ifpeople ergagintheseprocessesautomatieally since
they will have difﬁulty breaking out of the routines. Like changing the way someone
walks. Or, iftlreydoatterrrpttostoptheautonraticroutines, insteadofsaving
resources, they will potertially use more by having tocontrol a process which
otlrewise takes place automatically. LikeWise, one would expect people to have
limited insight into their own self-regulatory processes limiting the predictive ability of
self-report measures of self-regulation Automatic processes, particularly those which
are totally unconscious or have reached automaticity are difﬁcult for people to
articulate and describe. Again, this is consistert with the currert ﬁndings that self-
report measures of self-regation (in this ease metacognition) did not denonstrate
much predictive efﬁcacy. People my have had difﬁculty describing processes which
wee automatic. I

The liteature on self-regulation, and metacognition in particular, is ﬁought

124
with problem both methodological and theoretieal. Failure to control for irrrportarrt

andbettetmdestoodfactors (suchasability, ormotivationinforrrrssuchasgal
conrrnittrnert) may lead to an overestimation of the efﬁcacy of self-regulatory
manipulation like training. Also, the lack of theoretical speciﬁcity about the exact
mechanrsms' of self-regulation which are being assessed, measured, manipulated, and
comideedhas leadtocontradictory ﬁndings. Moreworkisneededintlris liturature
to establish effective guidelines for reseach in this area, and to develop our theoretical
clarity and undestandrn' g of self-regulatory constructs.

I l' . l E D' .

Thepresertstudyattenptedtoaddresssomeofthe implieationsanlimitations
of the limited resources model of skill acquisition The limited resornces model has
terded to be descriptive, not prescriptive, to maintain a linrited view of leaning and
performance, andhasnotaddressedwhatlearnesdowiththeirresorncesthatyielm
leaning

Toaddresstlreﬁrstproblenr, manipulationsweeusedwhichappeaed
comistert with the limited resources model. The limited resource model suggests that
self-regulation will improve pefonnance if the bereﬁts of metacognition outweigh the
costs in resources. This means that to be beneﬁcial in early skill acquisition,
nretacognition must be eﬁiciert and effective. This notion is consistent with the
ﬁndings that the quality, and not meely the quantity of metacognition appears to
relate to leaning and pefonnance. Presently, an interse training session was not

successﬁrl in bringing about irrrprovemerts in the quality of metacognition It may be

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125
that only longe peiods of training, or training on diﬁeent aspects of metacognition,
might be successful at improving the quality and efﬁciercy of metacognition such that
sufﬁciert resources are saved to beneﬁt pefornrance.

Nevetlreless, the currert ﬁndings suggest that a more efﬁcacious technique for
gereating increased metacognition or highe quality metacognition may be to invoke a
direct manipulation This would involve forcing the leanes to peform metacognition
on-line as they leanr. Metacognitive questions would be written directly into the task
training itself. For example, questions may be irnbedded in the mateial, or traines
may ask questions such as, “How eﬂ°ective is your currert strategy? How do you know
if it is working?” This method eliminates the transfe problem of the metacognitive
training, and people may lean the inrportarrt questions to ask tlrenselves with repeated
erqrosmetosuchanintervention Howeve,tlrismethodwouldrequiretrainesto
conside the irrrportarrt metacogrritive questions at each leaning stage, and ther write
them into the training and leave suﬂiciert time for leanes to conside these issues.
Or, thetrainenrayneedtobepreserttohelp guidethemetacognitiveprocesses(a
more “directed leaning’ approach). For some, ever asking the correct questions may
not be sufﬁciert since they cannot gereate realistic arrswes, they may need an
extenal guide to help them evaluate their own answers.

Thee was some eviderce of this type of meacognitive failure in the post-
scerario questionnaires when people evaluated their strategies incorrectly, adopting a
strategy that was based on inconect information or a poor evaluation oftheir own task

conrprelrersion It has beer said that a wise man knows what he does not know.

126

People with poor nretacogrritive skills, they may not know what they do not know
everiftlreyareaskedtothinkabout it. Reseachisneededtodeteminewhethe
metacognitive nrarripulations are in fact a more eﬂieacious and eﬂiciert way to
improve metacognition for skill acquisition Moreove, we must lean how much
guidance is needed to erable people with varying levels of metacogrritive skill to
effectively use such an irrtevertiorr.

Reseachisalsoneededtoaddressﬂreﬁmdamertalquestionofthelimited
resources model of skill acquisition: whethe self-regulation and/or metacognition are
automatic processes. First and foremost, greate- theoretical developnrert is needed
around the constrcuts of self-regulation and metacognition. A start would be nrapping
the speciﬁc activities of self-regulation onto the speciﬁc acticities that are
metacogrritive. Whee they ovelap, and whee they are separate. Ther, each speciﬁc
processoractivity mustbeunderstoodindividuallyastowhethetheyareresomce
consmrqrtive, and theefore whethe attenpting to train or manipulate then is a viable
altenative. Iftlrey are automatic processes, ther manipulating then in orde to
increase resources is not a viable avenue of research. They are not using erough
resources to eause a deﬁcit in pefonnance. Attempting to manipulate then theefore
will cause a problen because making automatic processes more controlled will
necessarilycreateresomcedeeemerts. Reseachistherneededtoaddresstlre
question ofhow to manipulate automated processes such that the strategies selected
automatieally are beter strategies for the task at hand This may mear making people

aware of their processes, inrproving them, and ther re-automatizing.

127
In orde to address the second shortcoming of the limited resource model, a

limited view of leaning and performance, the current study examined declarative and
procedural knowledge acquisition in addition to pefonnance orierted measures like
score. leaning declarative and procedural knowledge is the primary task in ea'ly
skill acquisition. Rather than a by product of pefonnance, declarative and procedural
knowledge play crucial roles in determining pefonnance. The currert ﬁndings
suggested that declarative and procedural knowledge may impact diffeent aspects of
pefonnance. Reseach is needed which will examine the possibility that declarative
and procedural knowledge play diﬂ‘eert roles in pefonnance, and how this ﬁnding, if
replicated, may aid in the diagosing problems and prescribing leaning activities to
renedy the problen Recognizing that declarative and procedural leaning wee
critieal, the currert study attempted to make this leaning more eﬂiciert using
sequerced gals aimed speciﬁcally at declarative and procedural knowledge
acquisition as instructional desig theorists (i.e. Gage) suggest. Particularly difﬁcult
todevelopanduseweeprocedural gals. Thegalnrarripulationdidnotseemtobe
able to increase procedural knowledge or activities aimed at increasing this knowledge.
Boththeformandtimingofprocedmal leaninggalsbeasfmtheexamination In
addition, reseach is needed that investigates the types of leaning activity that leads to
procedural knowledge (if in fact it is more than repetition), and how to help leanes
use these activities.

Theﬁnal shortcomingthisresearchaddressedwasanabserceofexplanationof
what leaners do with resources devoted to skill acquisition in orde to lean. The

128
limited resource model equates (for resource deperdent tasks) the devotion of

resources with improved pefonnance. Howeve, people devote resources to particular
activities which lead to leaning. These activities wee divided into metacognitive and
more direct leaning activities in the currert ﬁamework It appeaed that doing more
of these activities does not lead necessarily to irnprovemerts in pefonnance. The
quality, not quantity, of learning activity may be the key factor in effective leaning
and pefonnance. In this way, the devotion ofresomces alone is not suﬂiciert to
improve leaning Rathe, the activities to which resources are devoted is critieal.
Cognitive psychology may be helpful in discoveing what activities lead to rrraximal
leaning For exanrple, two activities like imaging and repetition may both require a
greatdealofresomces,butifimagingresultsinmoreretrievalcuesormoreeﬂiciert
“chunking” of the information, it would be expected to be a more effective use of
those resources. Theoretically driver reseach testing the effectiveress of diﬂeert
leaning activities is needed In the current study, few leanes explored diﬂemt
types of leaning activities. Participants appeaed to use strategies that eithe wee the
most obvious, or that they wee most comfortable with, despite adrnonitiom in training
to conside how effective their leaning strategy was. Reseach to determine how to
help people eithe choose effective strategies, or automate useﬁrl strategies (if these
processes indeed prove to be automatic) is needed

The efﬁciency with which enrployees acquire cognitively complex skills will
continue to be an important training comideation for organizations as they move into

the next certury. Theoretically grounded research on how to help enrployees lean

129
eﬂiciertly by making the most of their resources through effective self-regulation and

optimal learning strategies will, someday, form the link betweer skill acquisition
reseacharrdtrairringinorganizations. Thepresertstudyattenptedtotake some
initial steps toward this erd by investigating the training implications of one major
skill acquisition theory. Only follow up research on the most pronrising ways of
improving and measuring leaning and pefonnance will enable I/O Psychologists to
play a role in shaping training for organimtions in the future.

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APPENDIX A

146
APPENDIX A

ConsentFonn

The study in which you are about to participate is designed to examine how
individuals learn information and perform on a complex decision making task. You
will be asked toleam material, participate in learning exercises, and to perform on a
computer simulated radar tracking task in which you will measure the attributes of
targets on the screen, classify targets, and decide what action should be taken. You
will learntoprioritizedtargets anddecidewhichoughttobeacteduponﬁrst. In
addition, you will be asked to complete a short measure of cognitive ability. You will
answer questions about yourself, as well as about the tasks you will perform.

This experiment should last three hours. You will receive extra credit in your
psychology come for your participation in this experiment. At the end of the
experiment, you will be provided with written feedback explaining the purpose of the
research in more detail.

Your participation in this study is strictly voluntary. You are free to
discontinue participation in this experiment at any time for any reason without penalty.
Yom' responses will be kept completely conﬁdential. In addition, individuals will
remainanonymousinanyreportoftheresearchﬁndings. Youarefreetoaskany
questions you might have about this study at any time. You may ask questions about
the outcome ofthe research at any time by contacting Daniel Weissbein through the
Department of Psychology by mail or you may eall directly at 353-9166.

Bysigningthissheet, youagneethatthe researcherhasexplained thestudyto
youﬂiatyoumdastandtheprooedmesmbeusedmdﬂratyouﬁeelyconsernto

 

Signature:

 

 

APPENDIX B

147
APPENDIX B

Demographic Questions

Please answer the following questions about yourself. All answers are will be kept
strictly conﬁdential.

1. Age:—
2. Sex:

3. Overall GPA:

4. Have you ever participated in an experiment in this lab before?
Circle one: Yes No

6. How often do you play with video games?
_Never
_Rarely
_Sometimes
_Frequently
_A1ways

 

APPENDIX C

 

148
APPENDIX C

Individual Instructions

Duringthispartofthe experiment, youwill learningtooperateanavalradar .
tracking simulator. Your job is to learn the information contained in this handbook to
prepare yourself for the task

S'l I' Q'

You are the chief radar operator of the USS. Valor, a US. Navy Aegis class
missile cruiser. Your ship is cm'rently stationed in the Hondo Gulf where political and
military tensions are high

You are seated on the bridge of the ship where you ean receive information
ﬁ'omtheradarsensorsonacomputaizedradarscreen. Yourmissionistoprotect
you: ship ﬁ'om hostile targets, but avoid destroying peaceful targets. The decisions that
youmakearecritieal. YouarethemainadvisortotheCaptainastothecomseof
action you should take against targets. Incorrect decisions could plunge the Hondo
Gulfintoalloutwarandresultinthelossofthelivesofyouandyom'crew.

Yourship isatthe centeroftheradarconsole. Surroundingthe shiparea
numberoftargasrepreserrtedonﬂrescreenby asterisks. Thesensorsonyom‘ship
provide you with information you need to classify the targets according to their Type
(air, submarine, or surface vessel), their Class (civilian or military), and their Intent
(peaceful orhostile). YoumustﬁrstclassifythetargetasType air, submarine, or
surface. Then you must classify the target as Class civilian or military. And, third you
must classify the target's intent as peaceful or hostile.

Your goal is to protect the USS. Valor and the surrormding ﬂeet by correctly
i ' ' g the Type, Class, and Intent of the targets, and engaging them according to
their Intent That is, since the local govanment has been using both civilian and
military aircraft, subs, and surface vessels for maneuvers, so you should base your
Engagement response on the Intent of the target, peaceful or hostile. You should
advise the captain to "Shoot" any hostile targets and "Clear" any peaceful targets. You
must, however, make all four decisions (Type, Class, Intent, and Engage) accurately
for the Captain to respond correctly. For instance, if you identify the target as an Air,
Military, Hostile target and advise the Captain to Shoot, but the vessel was really
Surface not Air, the wrong weapons will be selected Therefore, you must make sure
that all forn' decisions are made correctly in order to eﬂectively respond to targets.

149

We

Your radar console looks like a computer. On the upper left corner you will
seethetimerernaininginthescenario. Whenthetimecounts downtozero(0)the
scenario is over. In the upper right comer of the console is the Score. You will
receive 100 points for a correct engagement ifALL FOUR decisions are made
correctly. IfANY of the four are made inconectly, you will loose 100 points. Your
job is ultimately to conectly engage as many targets as possible to get the highest
score.

At the center of the console, you see the USS. Valor, with a number of targets
surrounding the ship. In the Iowa left comer of the screen you see the radius that
your console is currently viewing. This indicates the distance from you ship ﬁre radar
is currently displaying. You may look further out away from your ship by clicking on
Zoom_Out on the OPER menu. You may focus the radar closer in by clicking on the
Zoom_In ﬁrnction on the OPER menu.

On the lower right comer of the console, you see the Hooked Track #. Each of
the targets on your console is given a number by the computer system on the Intrepid.
You can hook a target by placing the cursor on a target with the mouse and clicking
on it. The Hooked Track # display will show the number of that target The
informationyou gatherwillbe gathered forthattarget. Examine Figure 1 to seea
viewoftheradarscreenasitmightappearshortlyaﬁerthestartofagame.

Onthefarupperright comeroftheconsolearethewords OPER, TYPE,
CLSS, & ITNT. These are pull-down menus. These menus allow you to gather the
information you need to make the Type, Class, and Intent decision. The OPER menu
has operator functions like Zoom_1n, Zoom_Out, & Engage_ Shoot/Clear. The
console will not allow you to engage a target until you have classiﬁed the Type, Class,
and Intent. You must decide them in the following order“.

(1) ID Air/Sub/Sm'face
(2) ID Civilian/Military
(3) ID Peaceful/Hostile

150

To display the contents of a menu, you click the right mouse button on the
menu label. To display the information gathered by the sensors, place the pointer on
the item and press and HOLD the right mouse button. The infomration will be
displayed in the lower right hand corner until you release the mouse button. The
console menus contain the following items:

,.—__ _. _

OPER TYPE (ISS IINT

 

 

 

 

 

 

 

 

 

 

 

 

End_Simulation Speed Initial_Bearing Countermeasures !

Start_Sirrmlation AltituddDepth Initia1_Range Electronic_Warfare

Zoom_In C1ime/Dive_Rate Intelligence Threat_I.evel

Zoom_Out Si gnal_Strength Direction_of_Ori gin Respome

ENGAGE_Shoot/ Comrrnmieation_Time Maneuvering_Pattem Missile_l.cck

Clear

Range -I_D_Air/ Sub/ Surface ID_Civilian/Military ID_Peaceful/Hostile
M I . D . .

The ﬁrst ﬁve item on the TYPE, CLSS, and ITNT menus provide you with
the infomration you need to make the ID_Air/ Sub/ Surface, ID_Civilian/Militmy, and
ID_Peaceful/Hostile decisions. When you choose an item ﬁom these menus, you
receive the information value in the lower right comer of the console. There are rules
(value ranges) to follow to interpret this information. Pay close attention to these
rules. You must understand these rules to successfully engage targets. A "Review
Sheet" in this handbook will help you remember these rules.

The type menu allows you to gather information to determine if the target is
Air, Sub, or Surface. We will use infonmtion about the Target Speed to illustrate the
rules for making the ID_Air/ Sub/ Surface decision. Possible Speed values and their
meanings are as follows:

Speed > (greater than) 35 knots indicates an Aircraft (Type=Air)
Speed 25-35 knots indicates a Surface Vessel (T ype=Surface)
Speed 0-24 knots indicates a Submarine (T ype=Sub)

For example, the speed might be 115 knots. Since the target you have hooked
is traveling above 35 knots the target Type is Air. Remember that speed is only one
of ﬁve pieces of information you can use to make the determination of the Type. You
also need to look up the target's Altitude/Depth, Climb/Dive Rate, Signal Strength, and
Communication Time to determine the Type. It is up to you to determine when you
have enough information to make a decision.

 

151

Once you have looked at all ﬁve pieces of information to make the Type
decision, you will click the right mouse button on the ID Arr/Sub/ Surface button,
which allows you to identify the target as Air, Sub, or Surface according to your
determination. A list of your altematives will be displayed and you merely click on
your choice.

Once you have nude the decision, the symbol representing the target
will change ﬁom an asterisk to another symbol to correspond with your decision. Air
targets arerepresentedby upperhalfsymbols (n). Submarines arerepresented by
lower half symbols (u). Surface vessels are represented by whole symbols ([1).

When you make the Class decision, the symbols will change again to indicate
civilian or military. Civilian targets are represented by curved symbols. Military
targets are represented by sharp, angular, pointy symbols. The symbols are outlined
below.

 
 

 

 
 

Civilian
Military A

 

 

 

 

The ITNT menu contains the ﬁve pieces of information you need to determine
whether the target is peaceful or hostile. when you have gathered the ﬁve pieces of
information needed to make this decision you click on "ID_ Peaceﬁrl/Hostile" ﬁom to
bottom of this menu and click on yom decision to label the target as peaceful or
hostile.

After you have made decisions regarding the targets Type, Class, and Intent,
youarepreparedtomakeaFinal Engagement. Todothis, youwill openyouOPER
menu. Choose "Engage_Shoot/Clear" ﬁom this menu. Choose "Clear" for peaceful
targets and "Shoot" for hostile targets.

W

Unfortunately, not all of the information you get will be perfectly accurate.
For any given classiﬁcation decision (Type, Class, or Intent) you may get information -
thatisinconﬂictwithothercues. ThatisonecuemaytellyouthetargetisAirand
another cue might suggest the target is Sub! Or, one cue might suggest that the target
isMilitaryandanotherthatthesarnetargetisCivilian! Thiscanoccurbeeause
aircraft sometimes attempt to disguise themselves, or sensors read information wrong.
TodealwiththisOreAnﬁguity,youwillneedtomeasurecuestmtilatleastthree
agree. That is, three of ﬁve cues must agree for you to be conﬁdent of a decision.
For example, if the ITNT menu gave you the following values:

152

Countermeasures - None <= Peaceful
Electronic Warfare - Big Bulge Radar <= Hostile

Threat Level - 3 <= Hostile
Response - Inaudible <= Unknown
Missile Lock - Locked <= Hostile

You can see by the indications provided on the right that the Countermeasures
indieates that the target is Peaceful, the Response is Unknown, and the rest indicate
Hostile. In this ease, the answer is Hostile because three of ﬁve cues indicate hostile.
However, not until you had checked all ﬁve could you have determined the correct
threat level (assuming you checked them in order).

W

th you choose Shoot or Clear, if you hold down the right mouse button as
you choose, you will receive feedback in the lower right comer of the screen. This
will tell you the targets actual Type, Class, and Intwt, how you did, and whether you
gained or lost the 100 points.

Once you have engaged a target it will disappear from your screen, you have
no second chance to make the ﬁnal engagement You can reclassify the Type, Class,
orIntwtaslongasyoudosopriortotheﬁnalwgagemwt.

Don't forgetthatsince anytypeofvessel eanbeusedforhostilepm'poses,
unusual combinations like Air-Civilian-Hostile may arise, but only the Intent is
relevant for the Engage__Shoot/Clear decision. Likewise, Sub_Military_Peaceful
should be cleared

W

Manyﬁmes,tmgetsgormdetected1mﬁlﬂreymefairlyclose.Thiscanhappw
due to broken sensors, bad weather, low ﬂying aircraft, or stealth technology. You will
see some targets suddenly appear on your screw. Therefore, you will have to stay
alert because targets will "pop up" on your screw and may do so close to your ship or
theﬂeet. Thesecanbedangeroustoyou, soyoumustbeawareofthern.

W

In order to maintain a safe distance betwew targets and your ship, as well as
the ﬂeet, operational orders dictate that there are zones which you may not let targets
. These zones are ealled pwalty circles, and if a target wters the pwalty
circle, you will loose points.

153

The Inner Pwalty Circle is a circle indicated on the screw by a zone of 10
nmarormdyomshipcoveredinacriss—crosspattwr. Thiszonerepreswtsasafe
zone around your ship. If a ship gets in this zone you will loose 50 points. You
should prioritized the targets you choose to avoid letting targets into this zone as much
as possible.

The Outer Pwalty Circle is more complex, and more costly. The Outer Circle
represwts a zone around your whole ﬂeet. You must protect the ﬂeet as well as
yourself. The outer pwalty circle is INVISIBLE. It will not be shown on the screen.
However, it is at 128 nm A way to estimate the location of the outer circle is to
zoom out past 128 nm to 256 nm. The pwalty circle is now halfway betwew the
cwter ofthe radar and the edge ofyour screw. Note that zooming out to 128 will not
help you to idwtify the targets just outside of the penalty circle because they will be
outside ofyour viewing area. Therefore, zoom out to 256 nm. and estinmte halfway
betwewtheedgeofthescrewandthecwter. This istheOuteeralty Circle. Ifa
target pwetrates the Outer Pwalty Circle, it will cost you 150 points!

Remember that targets will appear suddenly on the screw, so you must
determineastrategytoguardthe innerandoutercircles. Onceatargethaspwetrated
thecircle, youhavelostthepoints forthattarget, itearmotgobackoutarrdre-wter
the circle.

Aﬁnal optionontheconsole locatedinthe OPEmeuistheRange.
Clicking this option will tell you a targets currwt range ﬁ'orn your ship. This is the
only cue value that will change over time (as a target moves closer or further away).
This will be helpful for you when deciding how to prioritized targets. Especially
whw used in conjtmction with Speed, Range ean help you determine which targets are
close to your penalty circles and fast

154

BMW

Type, Class, & Intent Information Values

Type - AidSuUSmface

[_T— -l_ SURFACE

 

Em

 

 

 

 

1 Speed > 35 knots 25-35 knots 0-24 knots
Altitude/Depth >Ofeet Ofeet <Ofeet ll
Climb’DiveRate >Ofeet Ofeet <Ofeet
Signal Strength Medium High Low
Communication 0-40 s 41-80 3 81-120 s

 

 

 

 

 

 

 

 

 

Imtial Bearing 1 91-270 deg 271-359 deg 0-90 deg
' Imtial Range I 0-20 nm 21-100 nm > 100 nm
Intelligence Platform Unavailable Private
Direction of Blue Lagoon Unknown Red Sea

Origin
Maneuvering Code Foxtrot Code Echo Code Delta

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX D

155
APPENDIX D

Metacognitive Training

Thinking About Your
Thinking!

 

 

156

Metacognitive Training

Today, you are being asked to learn the radar simulation you saw a few minutes ago.
Obviously, this simulation is complex: it has a number of different elements to learn
in order to do well such as the cue values, the penalty circles locations, etc.. In order
to do well, you will need to develop strategies to deal with learning and remembering
the necessary material, as well as strategies for dealing with the task elements
themselves, like cue conﬂict and penalty circles. In a few minutes you will be given
time to study the material to prepare yourself. But, it is a lot of material to learn, and
not a lot of time to learn it! You may feel like this when you are doing the
simulation:

 

In order to help you deal with all of the learning you will be doing, let’s take a few
minutes to talk about an element of learning that most people do not consider:

Thinking about your thinking!

Also called metacognition by some people (meta meaning “beyond” and “cognition”
meaning thinking). By using the things we talk about here when you start learning
the radar simulation information, you can help yourself to deal with all of the
learning you will be doing. This will help you do better at the task, and any learning
you do!

So, what do we mean when we talk about “thinking about your thinking”?

 

157

Metacognition can be thought of in
terms of asking yourself three basic
questions as you engage in the learning
process:

 

1 What do I need to know?

2 How can I make sure I
know the material well
enough?

3 How can I evaluate and
adapt my strategy to meet
my learning needs?

Let’s look at the 3 in order.

158

What is important:

Choosing what is important is a matter of choosing
which information:
you will need or use often
3 is critical to reaching your goal f

Once you choose what is important you can mark it
for special attention.

 

You probably do this when you highlight in your
text books for class. By highlighting, you mark what
material you want to give special attention when
studying, which makes you more efﬁcient! .
However, you probably do this without much
thought. Here, you should try to actively determine
what material is most important to you for the

radar task, and mark it with a star or by circling it.

When you begin to study for the Radar Task,

ask yourself: What will I use most often? What
should I know from memory to score well?

Then, focus your time on this material. Don’t waste
time with material that does not relate to scoring
points or that you will rarely use.

 

159

What is difﬁcult:

Determining what will be difﬁcult ahead of time

may also help you to be efﬁcient when you study.
Material can be difﬁcult to learn for a number of
reasons. It could be:

Unfamiliar Not Meaningful Technical
Detailed Very Similar

When you ﬁnd material that will be difﬁcult, but

is important , you should consider your
STRATEGY for learning.

What strategy do you typically use to memorize
information ?

 

You probably use this strategy without much
thought because it works for you most of the time.
But, for difﬁcult material, you may want to try
something different.

160

For instance, let’s say you wanted to remember
the number “31” because that’s how

many people are in your class. Often people

use repetition to learn. You could just say “31”
to yourself over and over. But, it might be better
if you associated the number 31 WITH something
that was more meaningful.

Like Baskin-Robins 31 ﬂavors.

You could picture yourself eating at “31 Flavors”
with your class! The point is that when you ﬁnd
material that is particularly difficult, using another
strategy, like association or imaging, will help you
to remember it. But, you must consider your
strategy and how well it is working !!!

Lets try an example of choosing what material
will be the most difﬁcult. On the next page, you
will see information about heliCOpters similar
to the kind you will need for the task.

Draw a star next to the categories you think would
be the hardest to memorize. Then, in the space
provided, write what it is you think would be hard
to learn about those categories!

161

HELICOPTER MCATTON MORMATION CHARTS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

W
HUEY APAGIE SIKORSKI
[ENGIH 30 Feet 20 Feet 40 Feet
ROTOR 5m: Long Short Medium
RoroR SPEED 6,000 RPM 10,000 RPM 3,000 RPM
RADARREIURN Partial Masked Full
mur Deep High Moderate
W
ﬂ CIVIIIANS ATTACK CARGO
| rm 3 1 2
I-OORRIDOR 0-53 >112 53-11'2
WEAPONS Unarmed Fully Armed Variable
- - 5mm None Goblin Stingray
NRA RED Visible Invisible Limited

 

Wehthreeeamgoriwrﬁdyouﬂaeeastarnextm,inﬁedingthattheywouldbeﬂre

lmdestto learn?
1.

2.

 

3.

Whatabouttimeeategorics nndeyouﬂrinktheywillbediﬁculttoleam?

l.

 

2.

 

3.

 

 

 

162

Before moving on, let’s try another exercise.

Study the “HelicopterUse” chart you looked at
earlier (listed again below). When you feel you
know the information on the chart, stop and answer
the questions on the following page.

 

 

 

 

 

 

 

 

 

 

W
CIVIUANS ‘ ATrAcx CARGO
1m 3 1 2
CORRIDOR 0-53 >112 53-112
WEAPONS Umrmd . Fully/Armed Variable
smALm Non_c Goblin Stingray
INFRA RFD Visible Invisible Limited

 

163

. The term “goblin” means the stealth is what?

 

. If the corridor status is 115, what does this mean?

 

. A cargo helicopter has what IF F ?

 

. If the weapons are “Variable” is this a civilian or Cargo Helicopter?

. If the Infra Red is 52, what is the helicopter used for?

 

164

How Can I Make Sure I Know
the Material Well Enough?

How did you do on the last exercise? The key

to success is to realize that once you have
determined which material is important to know,
and what will be difﬁcult to learn, you must go

about the critical process of making sure you
KNOW IT WELL ENOUGH.

You can think about this as looking for gaps
between what you need to know, and what you
do know!

 

 

 

Material you
need to know

Material you
Do Know

 

 

 

 

 

 

165
That is, you are looking for holes in your learning.
As you study, you must be monitoring your learning
to make sure you are, in fact, ﬁlling in these gaps.
Many times, people fail to actively do this, and they
then don’t know where they are weak. For example,
they may say “I studied, and I thought I knew the information
but then I only got a ‘C’ on the test.” They did not catch the
holes in their learning!

What student needs
to know for a test

What they know

 

Likewise, on the last exercise, you might have felt like you
knew the helicopter information, but then the questions
were harder than you thought, and you may have found you
didn’t know the information as well as you thought.

To answer the essential question “do I know the material well
enough?” you must determine how well you need to know

it, vs. how well you do know it. Then, you can look for the
gaps between the two!

166

Determining how well you need to know material.

To determine how well you need to know information, think
about the demands of the task. Do you need to know the details
of information, or just the gist of it? Do you need to memorize
information, or just be able to recognize it?

When studying, you should try to make your studying match
the requirements of the test. So, when studying for an essay test,
you would have yourself answer in essay form. The same holds
true as you begin to study the material for the radar simulation.
When you engage the radar task, you should think to yourself
how will I need to use the information? What does the task tell
me about a target, and what must I do WITH that information?
Then match your study to the way you will use the information?

What does the radar task tell you about the target’s location?
How will you need to use this? So, how should you learn it?

Once you have determined how well you need to know the
information, you can begin studying. Remember, some of
the more difﬁcult and important material you identiﬁed
may need a different strategy or some extra attention!

Once you have studied for a while, you then must begin
determining whether you know the information well
enough.

167

Making sure you know
the information well
enough!

 

Determining how well you actually know the material

you are studying is a matter of probing your knowledge

for gaps. One of the most effective ways to look for gaps

is to SELF-TEST. Self-testing can take many forms, but

it is basically forcing yourself to recall the information
without looking at it. One way to be more efﬁcient is to put
a mark like a star, a check, or a dot next to material

you get wrong as you are self-testing. Then you can go back
and focus on the material you had the most trouble learning.

An example from the helicopter material you studied earlier
would be if you turned over the paper and then forced yourself
to recall the information by testing yourself with questions like
“What 3 values go with stealth? Which value is for Civilians?”
Then you would turn the page and correct yourself, putting a
mark next to ones you got wrong. This way, you can give
special focus to the ones that are wrong, or can try a new
strategy to learn that information.

 

168

Let’s try some self-testing. Take 4 min. to study

the Helicopter Make chart below, copied from the
charts you saw earlier. You will be asked to identify
the make of helicopter associated with an attribute
value (i.e ‘ you will be told “Pitch is high” and you
will have to remember what kind of helicopter has

a high pitch). Test yourself as you are learning.

Put a mark next to information you have the most
trouble with. Also, don’t forget which information
you determined would be the most difﬁcult before!

 

 

 

 

 

 

W
HUEY APACHE SIKORSKI
1mm 30 Feet 20 Feet 40 Feet
MIOR sm: Long Short Medium
ROTOR SPEED 6,000 RPM 10,000 RPM 8,000 RPM
RADAR RETURN Partial Masked Full
P1101 Deep trig: Moderate

 

 

 

 

 

169

Now answer the following questions:

If the length is 20, is the helicopter a Huey, Apache, or Sikorski?
If the pitch is moderate, is the helicopter a Huey?

If the rotor speed is 10, 000 rpm, what kind of helicopter is it?
Which helicopter has a Partial radar return?

Is a short rotor associated with an Apache or Sikorski ?

Did you get them all correct? 1

Were you able to determine in advance which you were going
to have the most trouble with? Why or why not?

 

How can you use self-testing when studying the material for
the Radar Simulation task?

 

 

 

170

How can I evaluate and adapt my
strategy to meet my learning needs?

Finally, evaluating your strategy is an aspect of metacognition
that is important to learning the radar simulation. While
monitoring asks whether you are learning well enough,
strategy evaluation asks whether the methods or approach you
are using to perform is bringing you closer to your goal.

Here we are talking about your strategy for learning and
performing on the task.

Often, when people begin a task they simply apply the ﬁrst
strategy or approach to the task that comes to mind, like trial
and error. They generally don’t consider whether it is

a good strategy, and don’t consider whether it is working.

For the radar simulation, there are many possible strategies for
cue conﬂict, penalty circles, and other aspects of the
simulation. You should think about what approach you

are using for dealing with these elements, and whether it

is working. Remember, you will have 6 trials to work

on the simulation. This means you will have chances to try to
adapt your strategies.

You must consider your choice of strategy, whether it is working
and how to improve it!

171

1. What strategy am I using?

The ﬁrst step is to determine what strategy

you are using or are going to use. Many times
people use strategies without thinking about what
they are doing. For example, trial and error is often
used without considering other strategies. You
should give some conscious thought to what
strategy you are using to reach your goals.

2. Is it working well?

Step two is to consider after a period of time if
your strategy is successful for you. Beginners

will often fail to think about whether their approach
is successful. They tend to stay with poor

strategies too long, or switch from good ones

too fast. You should consider how long yours will
take to work.

You may have to consider how you will know
if it is working! The computer gives you some
information like if you go a target correct. But, this
does not tell you necessarily whether your strategy
is working. You will have to determine for

 

 

172

yourself what information you need to

tell you if your approach is working. Such as:
-how often targets go into your penalty circles,
-whether you are going faster,

-whether your score is improving, etc.

3. HOW CAN I IMPROVE IT?

The third step is to think about how to improve

your strategy. What can you do to change or alter
your approach that will overcome the difﬁculties

you encountered? This may mean slight

“tweaks” to your approach, or it may mean wholesale
changes in your strategy. In either case, before you
begin the next trial, you should have considered what
you will do differently.

 

 

APPENDIX E

 

 

 

173

APPENDIX E

Metacognitive Activity Questionnaire

 

1 2 3 4 5 6 7

l l l l l l !
Strongly Neutral Strongly
Disagree Agree

Next to each statement, please indicate how much you agree or disagree with the
statement baseed upon your behavior during the study period and on the task.

1. I skimmed the material provided ﬁrst to determine what would be the
most difﬁcult for me to learn.

2. As I studied the material, I marked material that would be difﬁcult for
me to learn so that I could concentrate on it (e.g. highlight, star, etc).

3. I tested myself regarding how well I knew the cue values.

4. I DIQNQI test myself as to how well I knew the location of the
penalty circles.

5. I consciously attempted to create a strategy to deal with the task.

6. 1 1211115191 purposefully attempt to determine what strategy I was
using to learn about the task.

7. I consciously thought about what strategy I was using to learn the cue
values.

8. I consciously evaluated how well my task strategy or strategies were
working.

9. I consciously thought about howl could improve my strategy.
10. I thought about how long my task strategy should take to show results.

1 1. I attempted to monitor how well I was learning as I studied the
material.

174

Appendix E (Cont'd)

_12. I planned how to learn as much of the material as I could in the time
allowed.

_13. I thought about how I would need to use the material I was studying.

 

 

 

APPENDIX F

 

 

 

goal is
ex .21
but 21!
scores

little l
points
oombi

 

.VOllr r

0f the

175
APPENDIX F

GoalDirections
BE 3 III .
GOALl—SCENARIOSIANDZ

On the ﬁrst two scwarios, we would like you to reach a particular goal. Your
god is to get 450 poirm on the next two scenarios combined. That is, you should
get-200to-250 pointsoneachofthe scwariostofollow. This isachallwginggoal,
but about 10% ofthe previous people who have done this task have attained these
scores.

GOAL2—SCENARIOS3AND4

Now that you have had some practice with the task, we can set your sights a
little higher. For the next two scwarios, your goal is to reach the score of 100
poirls. Thatis,youshouldtrytogettothescoreoflOOacrossthenexttwoscwarios
combined For example, by getting -100 on scwario 3 and thw 200 points on
scenario 4. This is a challwging goal, but 10% of the previous people who lmve done
this task have attained these scores.

GOAL 3— SCENARIOS 5 AND 6

Time for yom' ﬁnal goal for the last two scwarios. For the next two scwarios,
your-goal is to reach the score of -250 points. That is, you should try to get to the
score of -250 across the next two scwarios combined For example, by getting 0 on
scwario 5 and thw -250 points on scwario 6. This is a challwging goal, but 10%
of the previous people who have done this task have attained these scores.

 

ﬂ.‘

176
5 IS! ID. .

GOAL 1
SCENARIOS 1 AND 2

Itis irnportanttohaveagoodgraspofthe infonnationneededtomakedecisions. A
solidgraspofthis infonnationwillwableyoutoperformbetterlaterinthetask.
Therefore, yorn' goal over the next two scenarios is to commit the m an!
location of the WEB tn memory.

You should focus your attwtion on the Review Sheet provided to you and memorize
the values for the cues. The idea is that memorizing these now will mean you won't
havetowasteyourtimelookingupthevalues onthereviewsheeteachtimeasyou
go. For any givw value of a cue, you should be able to tell without looking what that
means about the target. For example, if you are told by the simulation "the Speed is
54 knots" you should know without looking that this cue value suggests the target is
type AIR This will allow you to concwtrate more of your time and effort on

perusing targets.

In addition, you should make sure you know the correct loeation of the pwalty circles
andwheretozoomto wgagethem

 

 

177

GOALZ
SCENARIOS 3 AND 4

Now that you have had some experiwce with the task and have learned the cue values
and pwalty circle locations, it is time to understand the relationships betwew the task
elemwts. One way to do this is to think about the task in terms of if-thw
relationships. That is, as action and reaction, or situation and action. So, for example,
"if it is icy out and you want to stop, thw you should pump the brakes of your car."
This would be a statemwt about the elernwts of the situation (it is icy out and you
want to stop) and the reaction (pinup your brakes).

Yom'goalhereisto gweratethebestsetofrulesthatwillhelpyoutounderstandthe
task, particularly for the things you will have to make choice about such as target
prioritimtion. Trytogwerateascompleteasetasyoucan Agairrdonotmrsm
mm other than as feedback for helping you understand the relationships
between task elemwts, or your actions and the results.

 

 

178

Read over the following 10 situation. Try to develop and wd to these 10 staternwts
over the next two trials. That is, try to understand what actions or decisions these
situations would eall for to get the best score later whw you are trying for points.

Iftheﬁrst3 cues inadecisionagree,
thw

 

If Iwanttochoosewhichtargettowgagenext,
thw

 

If a target is moving faster than other targets,
thw

If you have a choice betwew persuing a target threatening the inside pwalty circle
and the outside pwalty circle,
thw

If one target has pwetrated a penalty circle but one has not,
thw .

 

If a target is not near either pwalty circle, thw
thw

 

If you have bew wgaging targets near the inner pwalty circle for a while,
thw .

 

If you want to ﬁnd targets near the outer penalty circle,
thw

 

If there are many targets threatening both pwalty circles,
thw .

 

Ifyou are wgaging targets too slow, thw

 

 

179
.GOALB—SCENARIOSSAND6

Now that you know the information and understand the task, it is time to
perform! Now your score counts! Time for your ﬁnal goal for the last two scwarios.
For the next two scwarios, yom' goal is to reach the score of -250 points. That is,
you should try to get to the score of -250 across the next two scenarios combined
For example, by getting 0 on scwario 5 and thw -250 points on scwario 6. This
is a challwging goal, but 10% of the previous people who have done this task have
attained these scores.

 

 

APPENDIX G

 

 

Go:

Cll't

(T7

180

APPENDIX G
Goal Committrnent Questionnaire
Circle the number that indicates your commitment to the goal you were assigned.

1. How hard are you willing to work to achieve the assigned goal?

 

1 2 3 4 5 6 7 8
l l l l l I l I
I I I I I I I I
Exteremely Not At
Hard All Hard

2. I am willing to put forth a great deal of effort to reach my assigned goal.

 

1 2 3 4 5 6 7 8

l l l l l l l l

I I I I I I I I
Strongly Strongly
Agree Disagree

3. How committed are you to working as hard as possible to reach the assigned
goal?

1 2 3 4 5 6 7 8

I l l l l l l J
I I I I I I I

Extremely Extremely
Committed Uncommitted

 

 

.dlv.

APPENDIX H

 

 

to

181
APPENDIX H

Knowledge Test

Please circle the letter corresponding to the correct answer.

1.

The location (in nautical miles) of the outer pwalty circle is:

a. 512

b. 256

c. 10

d 128

An initial bearing of 85 indicates that the target is:

a. Unknown

b. Hostile

c. Military

d Civilian

The Code Delta Maneuvering pattern indieates which of the following? The
target is:

a. Unknown

b. Sub

c. Civilian

d Military

A submarine might have which of the following Communication Times:
a. 41

b. 110

c. 40

d 50

The threat level which would indicate a hostile target is:

999‘s»
thr-r

 

 

10.

182
Which of the following is an indication that the target is Hostile?

Direction of Origin = Red Sea
Maneuvering pattern = Code Echo
Response = Givw

Missile Lock = Locked

9.0 92‘s»

Ifa targa is outside the range ofyour screw your best option to view the
target isto:

a. Wait for it to come into view.
b. Ignore it if it is outside of the view beeause it cannot cause problems
that far away.

c. Zoom out to wgage the target.
d. Thereisnothingyoueandotowgagethetarget

Assmnethatatthestartofthe scwariotherearetwotargetsonebyeach
pwalty circle. Both targets are traveling at 250 knots and are 13 nm from their
respective pwalty circles. Which target should you wgage ﬁrst?

the target near the inner pwalty circle

the target near the outer penalty circle

it does not matter, both are equal

wgage the target with the highest Initial Range

9.0 9‘1»

If you notice three targets clustered together near a penalty circle, how will you
determine which to wgage ﬁrst?

a. Check the Initial Ranges

b. Check the Speeds

c. Check both Range and Speed

(1 Zoom outto 516theouterpwalty circle and wgage the target closest
to the cwter (your own ship)

Which menu would you select to Zoom Out?

a. Operator
b. Type

c. Class

(I. Intent

 

 

11.

12.

13.

14.

15.

16.

183

Whatistheminimmnnmnberofcuesneededtowsmeaconecttypedecision?

 

a. 1

b. 2

c. 3

d 4

An Initial Range of 300 nm indicates that the target is: re.
a Air '

b. Military

c. Civilian

d Sln'face .

A target with which of the following could be Military? A target with: II

3. Initial Range = 10nm

b. Maneuvering Pattern of Code Foxtrot.
c. Initial Range = 110nm.

Direction of Origin = Blue Lagoon

9.

"Big Bulge Radar" is the Hostile value signal for which of the following
attributes: -

Electronic Warfare
Countermeasures

Response
Intelligwce

a.
b.
c.
d
What is the fastest possible speed for a surface vessel?

a. 25 knots
b. 24 knots
c. 35 knots
d 34 knots

Surface Vessels could have a signal strength of:

a 1

b. High

c. Medium
d Iow

 

184

17. If you have just wgaged three targets near the Inner Pwalty Circle, which of
the following should you do next?

Engage the next target nearest to the Inner Pwalty Circle
Engage the fastest target near the Inner Pwalty Circle
Zoom out to check the Outer Pwalty Circle.

Zoom in to check how close targets are to yom' own ship.

53-957!”

18. If you have three targets,
target (a) with Range = 120 nm, Speed =150 knots
target (b) with Range = 130 nm , Speed = 100 knots, and
target (c) with Range = 8 nm, Speed =150 knots,

which target should you wgage ﬁrst?
a target a

b. target b

0. target c

d. any order, it will not matter

19. If you have three targets,
target (a) with Range = 8 nm, Speed = 150 knots
target (b) with Range =12 nm , Speed = 150 knots, and
target (c)withRange=8nm, Speed=200knots,

which target should you wgage ﬁrst?
a. target a

b. target b

c. target 0

d any order, it will not matter

20. Suppose you had the following three targets:
target (1) which is air, military, and hostile at 125 nm
target (2) which is air, civilian, and peaceful at 9 nm
target (3) which is sub, miliary, hostile at 5 nm

which target would you wgage ﬁrst?

a. target 1

b. target 2

c. target 3

d it does not matter which target based upon this infomration

 

 

 

APPENDIX I

 

LI

i Q

A.)

185

APPENDIX I

Learning Activity Questionnaire

 

Circle the number corresponding to the answer that best represents the frequency
or amount that you did the following:

1. To what extent did you use mental rehearsal to learn the material contained in
the handbook and review sheet (e. g. cue values, penalty circle locations, etc.)?

 

1 2 3 4 5 6 7

l I. l- .L i I 1

Very To Some A Great
Little Extent Deal

 

2. To what extent did you list (as in write several times) material contained in
the handbook and review sheet to help you remember this information?

 

l 2 3 4 5 6 7

l ! l l ! ! I

Very To Some A Great
Little Extent Deal

3. To what extent did you use mnemonic devices to help you remember material
contained in the handbook and review sheet?

 

l 2 3 4 5 6 7

| l l l l l 1

Very To Some A Great
Little Extent Deal

4. To what extent did you underline information contained in the handbook or
review sheet that you wanted to learn?

1 2 3 4 5 6

l l l l l
l I I I I

Very To Some Great
Little Extent Deal

>"'\l

 

5. l
reve

 

lear

SCC

pr

186

5. To what extent did you associate information contained in the handbook or
reveiw sheet that you wanted to learn with something you already knew?

 

l 2 3 4 5 6 7

p I I I I I I

Very To Some A Great
Little Extent Deal

6. To what extent did you think about how to apply information you were ‘

 

learning to the task itself?
1 2 3 4 5 6 7 I
I I I I I I I
Very To Some A Great .
Little Extent Deal

7. How often did you hook targets just to practice leanring cue values, not to
score points?

 

l 2 3 4 5 6 7

I I I I I I I

Very To Some A Great
Little Extent Deal

8. How often did you check a target's range and speed just to practice
prioritizing targets not to score points?

 

1 2 3 4 5 6 7

I I I I I I I

Very To Some A Great
Little Extent Deal

9. How much time did you spend exploring the task to gain an understanding of
the task not trying to score points?

1 2 3 4 5 6 7

| I I I I I I

Very Some A Great
Little Deal

10.
and:

11.

lee

187

10. How much time did you spend trying to understand the abstract concepts
underlying the task?

 

1 2 3 4 5 6 7

I I I I I I I

Very Some A Great
Little Deal

11. To what extent did you spend time trying to observe and reﬂect upon the
task?

 

1 2 3 4 5 6 7

I I I I I I I

Very To Some A Great
Little Extent Deal

12. To what extent did you try and summarize the material you were trying to
learn in your own words?

 

1 2 3 4 5 6 7

I I I I I I I

Very To Some A Great
Little Extent Deal

13. To what extent did you try and devise a system for recalling the important
material?

 

l 2 3 4 5 6 7

I I 4 I I I I

Very To Some A Great
Little Extent Deal

14. To what extent did you try and think of rules of thumb for the task?

1 2 3 4 5 6 7
I I I I I I I

l I I I I I

Very To Some A Great
Little Extent Deal

 

188

15. To what extent did you try to generate metaphors which would help you learn
the material or understand the task?

 

1 2 3 4 5 6 7

l I I I I I 4.

Very To Some A Great
Little Extent Deal

16. To what extent did you generate and test hypotheses to help you learn about
the task?

 

1 2 3 4 5 6 7

I I I I I I I

Very To Some A Great
Little Extent Deal

17. To what extent did you experiment with a new strategy to see if it would
work?

Very To Some A Great
Little Extent Deal

FL“.

 

 

APPENDIX J

 

1 89
APPENDIX J

Post Scwario Questions
Please indicate on the following line what exactly you did during the last scenario to

reach your goal. Be as speciﬁc as possible as to what you were trying to accomplish F“
and how you attempted to do this. "

Scwario]

 

 

 

 

 

ScwarioZ

 

 

 

 

Scenario3

 

 

 

 

 

Scwario 4

190

 

 

 

 

ScwarioS

 

 

 

 

Scwario6

 

 

 

 

 

 

APPENDIX K

 

 

191

APPENDIX K

Debrieﬁng Sheet

The experimwt in which you just took part was designed to examine the
effects of various types of goal and training on the acquisition of a complex skill. The
study is comparing the learning and performance of people who receive training on
metacognition (thinking about thinking) to those who do not receive this training in
ordertodetwnine ifthistypeoftrainingcanwhanceleamingandperformancelmder
either performance goals or sequwced subgoals. Prior research elsewhere had
questioned the usefulness of goals early in skill acquisition, and this research examines
how to make goals useful during this time. This research has implications for helping
people to more efﬁciwtly use their resources to acquire complex skills, such as the
task you performed '

If you have any concerns or questions, or you would like to learn the results of the
study, you can write to Daniel Weissbein in 129 Psychology research, or call directly
at 353-9166.

 

APPENDIX L

 

 

1 92
APPENDIX L

InstructionstoRaters

Please rate the subjects’ responses on the Post Scwario Questiomlaire in terms ofthia
metacognitive content. I would like you to use a 1 to 7 fonnat, with 1 being the
lowest possible rating. You will have to use your best judgmwt, some people will not
have much to rate (some people will have no metacognitive content of course). You
are looking for the overall quality of their metacognitive activity based on yorn'
examination of their writing. As you know, metacognition involves thinking about
thinking. On the Post Scwario Questionnaries, this is likely to take the following
forms:

TYPE: EXAMPLES:

Strategy evaluation -1 did X but it did not seem to work
-My strategy allowed too many to go into the inner circle

Idwtiﬁcation of -I did not know the important information

ﬂaws or irnprovernwt -I know the cues better

in knowledge -The Type decision is harder than the other decisions for
me

Identiﬁcation of

difﬁcult information -I am having trouble learning the Initial Bearing

Self-Testing -I used self-testing to learn the cues
-I forced myselfnot to look at the review sheet as I

pmgressed

Strategy Change

due to ﬂaw -Since before I was missing the ones in the inner circle,
now I will try the outer more

Others -misc. things you detemrine to indicate diagnosis and
prescription for cognitive shortcomings

Notes on rating:

. Having a strategy is not metacognitive

. Just saying “I wwt faster” or “I need to go faster” is not metacognitive unless
you see it as a strategic point

. Not looking at the review sheet only counts if it is on purpose or noted as an
assessmwt of knowledge

. “I’m doing well” only counts if it is tied to a strategy evaluation

. It is NOT metacognitive if they are saying they did well AT a strategy

 

APPENDIX M

 

V_ESCC Shh“. <ub£<c(r\ (chi Currant

Mediation tests - Metacognitive training on knowledge through Metacognitive activity

Hierarchical Regression Results of the Test of Hypotheses 8a — The mediation of the
effects of Metacognitive Training on Declarative Knowledge Through Metacognitive

193

APPENDIX M

 

Activity
VARIABLE [3 AR2 R2
EQUAHQLLI
Step 1:
Cognitive Ability .30" .121** .12**
Goal Commitment .07
Step 2:
MC. Activity (tally) -.08 .013 .13*
MC. Activity (score) .04
MC. Activity (rating) .08
Step 3:
MC. Training -.06 .002 .14*
STEP 1:
Cognitive Ability .30" .121** .12**
Goal Commitrnwt .07
STEP 2:
MC. Training -.06 .007 .13**
STEP 3:
MC. Activity (tally) -.08 .008 .14*
MC. Activity (score) .04
MC. Activity (rating) .08

 

*p<.05, **p<.01
n = 106

Fain.

 

 

194

Hierarchical Regression Results of the Test of Hypotheses a - The mediation of the
effects of Metacognitive Training on Procedural Knowledge Through Metacognitive

 

Activity
VARIABLE [3 AR2 R2
WI
Step 1:
Cognitive Ability .31" .146** .15"
Goal Commitrnwt .05
Step 2:
MC. Activity (tally) -.06 .026 .17*
MC. Activity (score) .07
MC. Activity (rating) .18
Step 3:
MC. Training 04 .001 .17*
EQUATION}
STEP 1:
Cognitive Ability .31" .145" .15**
Goal Commitrnwt .05
STEP 2:
MC. Training 04 .000 .15**
STEP 3:
MC. Activity (tally) -.06 .028 .17*
MC. Activity (score) .07
MC. Activity (rating) .18

 

*p<05, **p<.01
n = 106

 

APPENDIX N

 

195
APPENDIX N

Mediation Tests - The effects of Metacognitive Training on Performance Variables

Hierarchical Regression Results of the Test of Hypotheses 8b — The mediation of the
effects of Metacognitive Training and Activity on Score Through Knowledge

 

VARIABLE B AR2 R2
EQUAIIQNJ

STEP 1:

Cognitive Ability -.05 .064* .06*
Goal Commitrnwt .04

STEP 2:

Declarative .29" .166" .23"
Procedural .30"

STEP 3:

MC. Activity (tally) -.06 .021 .25"
MC. Activity (score) .05

MC. Activity (rating) .14

STEP 4:

MC. Training -.07 .004 .26"
EQUAIIQELZ

STEP 1:

Cognitive Ability -.06 .064* .06*
Goal Commitrnwt .04

STEP 2:

MC. Training -.07 .017 .08*
STEP 3:

MC. Activity (tally) -.06 .040 .12*
MC. Activity (score) .05

MC. Activity (rating) .14

STEP 4:

Declarative .29“ .135" .26**
Procedural .30**

 

*p<.05, **p<.01, n = 106

 

196

Hierarchieal Regression Results of the Test of Hypotheses 8b - The mediation of the
effects of Metacognitive Training and Activity on Correct Decisions Through

 

 

Knowledge
VARIABLE [3 AR2 R2
EQUAIIQIELI
STEP 1:
Cognitive Ability -.06 .081 .08
Goal Commitment .12
STEP 2:
Declarative .31" .16" .24**
Procedural .25“
STEP 3:
MC. Activity (tally) -.18 .045 .28“
MC. Activity (score) .02
MC. Activity (rating) .19
STEP 4:
MC. Training -.07 .005 .29“
W2
STEP 1:
Cognitive Ability -.06 .081 .08*
Goal Commitment .12
STEP 2:
MC. Training -.07 .028 .11"
STEP 3:
MC. Activity (tally) -.18 .057 .17**
MC. Activity (score) .02
MC. Activity (rating) .19
STEP 4:
Declarative .25“ .122" .29**
Procedural .31"
*p<.05, **p<.01

n=106

 

197

Hierarchical Regression Results of the Test of Hypotheses 8b — The mediation of the
effects of Metacognitive Training and Activity on Outer Penalty Circle Intrusions

 

 

through Knowledge
VARIABLE B AR2 R2
EQUATIQIELI
STEP 1:
Cognitive Ability -.06 .046 .05
Goal Comrnitrnwt -.03
STEP 2:
Declarative .08 .060" .1 1*
Procedural .27"
STEP 3:
MC. Activity (tally) -.18 .027 .13*
MC. Activity (score) -.10
MC. Activity (rating) .14
STEP 4:
MC. Training .07 .004 .14
W2
STEP 1:
Cognitive Ability -.06 .046 .05
Goal Cornrnitmwt -.03
STEP 2:
MC. Training -.07 .001 .05
STEP 3:
MC. Activity (tally) -.17 .028 .08
MC. Activity (score) -.10
MC. Activity (rating) .14
STEP 4:
Declarative -.08 .061* .13
Procedural -.26"‘
*p<.05, **p<01

n=106

 

 

Hierarchical Regression Results of the Test of Hypotheses 8b - The mediation of the
effects of Metacognitive Training and Activity on Inner Pwalty Circle Intrusions

198

 

 

through Knowledge
VARIABLE B AR2 R2
EQUAIIQNJ
STEP 1:
Cognitive Ability .02 .006 .01
Goal Commitrnwt .18
STEP 2:
Declarative -.03 .015 . 02
Procedural .15*
STEP 3:
MC. Activity (tally) -.05 .075* 10
MC. Activity (score) -.27*
MC. Activity (rating) -.03
STEP 4:
MC. Training .16 .020 .12
EQUAIIQIELZ
STEP 1:
Cognitive Ability .02 .006 .01
Goal Comnlitrnwt .18
STEP 2:
MC. Training .16 .027 .03
STEP 3:
MC. Activity (tally) -.05 .064 .10
MC. Activity (score) -.27*
MC. Activity (rating) -.03
STEP 4:
Declarative -.02 .020 .12
Procedural .15
*p<.05, **p<.01

n=106

 

 

APPENDIX 0

 

 

199

APPENDIX O

Mediation Tests - The effects of Goal Type on Knowledge

Hierarchical Regression Results of the Test of Hypotheses 9a — The mediation of the
effects of Goal Type on Declarative Knowledge through Learning and Metacognitive

 

Activity
VARIABLE B AR2 R2
W
STEP 1:
Cognitive Ability .30“ .146" .15"
Goal Cornmitrnwt .06
STEP 2
Learning Activity (tally) -.06 .037 .18"
MC. Activity (tally) -.04
Learning Activity (score) -.04
MC. Activity (score) .07
MC. Activity (rating) .18
STEP 3:
Goal Type -.09 .008 .19*
EQIJAIIQIELZ
STEP 1:
Cognitive Ability .29“ .146" .15“
Goal Commitmwt .05
STEP 2:
Goal Type .13 .013 .16"
STEP 3:
MC. Activity (tally) -.04 .031 .19*
Learning Activity (tally) -.06
Learning Activity (score) -.04
MC. Activity (rating) .18
MC. Activity (score) .07

 

*p<.05, **p<.01
n = 106

 

200

Hierarchical Regression Results of the Test of Hypotheses 9a - The mediation of the
effects of Goal Type on Procedural Knowledge through Learning and Metacognitive

 

Activity
VARIABLE B AR2 R2
STEP 1:
Cognitive Ability .02 .121“ .12**
Goal Commitrnwt .18
STEP 2
Leanring Activity (tally) -.13 .021 .14
MC. Activity (tally) -.10
learning Activity (score) .01
MC. Activity (rating) .12
MC. Activity (score) .05
STEP 3:
Goal Type .13 .016 .16*
STEP 1:
Cognitive Ability .29“ .121** .12**
Goal Commitrnwt .05
STEP 2:
Goal Type .13 .008 .13*
STEP 3:
MC. Activity (tally) -.10 .029 .16*

Learning Activity (tally) -.13
Learning Activity (score) .01

 

MC. Activity (score) .05
MC. Activity (rating) .11
*p<.05, **p<.01

n=106

 

 

APPENDIX P

17

77

 

 

201
APPENDIX P

Mediation Tests - The effects of Goal Type on Perfonnance Variables

Hierarchical Regression Results of the Test of Hypotheses 9b — The mediation of the
effects of Goal Type on Score through Learning Activity, Metacognitive Activity, and

 

Knowledge
VARIABLE B AR2 R2
EQUAIIQIELI
STEP 1:
Cognitive Ability -.04 .064* .06"
Goal Commitrnwt .06
STEP 2:
Declarative .32“ .166" .23**
Procedural .28"
STEP 3:
Leanring Activity (tally) -.08 .026 .26“
MC. Activity (tally) -.09
Learning Activity (score) -.07
MC. Activity (score) .05
MC. Activity (rating) .15
STEP 4:
Goal Type -.17 .026 .28**
EQUAIIQNJ
STEP 1:
Cognitive Ability -.04 064* .05*
Goal Commitrnwt .06
STEP 2:
Goal Type -.17 .025 .09*
STEP 3:
MC. Activity (tally) -.08 .054 .14"‘
Learning Activity (tally) .08
Learning Activity (score) -.07
MC. Activity (score) .07
MC. Activity (rating) .15

 

 

202

 

Appwdix P (Cont'd)

VARIABLE B AR2 R2
STEP 4:

Declarative .32” .139** .28**
Procedural .28"

 

*p<.05, **p<01, n = 106

 

203

Hierarchical Regression Results of the Test of Hypotheses 9b — The mediation of the
effects of Goal Type on Number Engaged through Learning Activity, MC. Activity,

 

and Knowledge
VARIABLE B AR2 R2
W
STEP 1:
Cognitive Ability .00 .042 .04
Goal Commitment .13
STEP 2:
Declarative .16 .037 .08
Procedural .10
STEP 3:
Learning Activity (tally) .06 .048 .13
MC. Activity (tally) -.21*
Leaning Activity (score) -.11
MC. Activity (score) .09
MC. Activity (rating) .12
STEP 4:
Goal Type .09 .008 .14
EQUATION}
STEP 1:
Cognitive Ability .00 .043 .04
Goal Commitment .13
STEP 2:
Goal Type .09 .011 .05
STEP 3:
MC. Activity (tally) -.21* .055 .11
Learning Activity (tally) .06
Leaning Activity (score) -.11
MC. Activity (score) .09
MC. Activity (rating) .12
STEP 4:
Declarative .16 .027 .14
Procedural .10

 

*p<.05, **p<.01, n = 106

204

Hierarchical Regression Results of the Test of Hypotheses 9b — The mediation of the
effects of Goa] Type on Correct Decisions through Leanring Activity, MC. Activity,

 

and Knowledge
VARIABLE B AR2 R2
W1
STEP 1:
Cognitive Ability -.05 .08" .08"
Goal Comrnitmwt .14
STEP 2:
Declarative .33" .157" .24"
Procedural .25"
STEP3:
Leaning Activity (tally) .11 .061 .30“
MC. Activity (tally) -.22*
Leaning Activity (score) -.12
MC. Activity (score) .07
MC. Activity (rating) .20
STEP 4:
Goal Type -.08 .005 .30"
W2
STEP 1:
Cognitive Ability -.05 .081" .08”
Goal Comrnitrnwt .14
STEP 2:
Goal Type -.08 .003 .08*
STEP 3:
MC. Activity (tally) -.21"‘ .091 .18*
Leaning Activity (tally) .11
Leaning Activity (score) -. 12
MC. Activity (score) .07
MC. Activity (rating) .20
STEP 4:
Declarative .33" .129" .30"
Procedural .25"

 

*p<.05, **p<01, n = 106

 

205

Hierarchieal Regression Results of the Test of Hypotheses 9b - The mediation of the
effects of Goal Type on Pwalty Points through Leanring Activity, MC. Activity, and

 

Knowledge
VARIABLE B AR2 R2
EQUAIIQNJ
STEP 1:
Cognitive Ability .00 .030 .03
Goal Comrnitrnwt .13
STEP 2:
Declarative -.09 .037 .07
Procedural -. l7
STEP 3:
Leaning Activity (tally) .04 .055 .12
MC. Activity (tally) -.13
Leaning Activity (score) .00
MC. Activity (score) -.21
MC. Activity (rating) .07
STEP 4:
Goal Type .04 .001 .12
STEP 1:
Cognitive Ability -.07 .034 .03
Goal Connnitrnent .06
STEP 2:
Goal Type .04 .005 .04
STEP 3:
MC. Activity (tally) -.14 .059 .09
Learning Activity (tally) .04
Leaning Activity (score) .00
MC. Activity (score) -.22
MC. Activity (rating) .07
STEP 4:
Declarative -.09 .029 .12
Procedural -.17

 

*p<05, **p<.01, n = 106

 

206

Hierarchical Regression Results of the Test of Hypotheses 9b - The mediation of the
effects of Goal Type on Outer P.C. Intrusions through Leanring Activity, MC.
Activity, and Knowledge

 

VARIABLE B AR2 R2
W1

STEP 1:

Cognitive Ability -.07 . .046 .05
Goal Commitment -.03

STEP 2:

Declarative -.09 .059* .1 1*
Procedural -.26*

STEP 3:

Leaning Activity (tally) .01 .028 .13
MC. Activity (tally) -.15

Learning Activity (score) .02

MC. Activity (score) -.11

MC. Activity (rating) .11

STEP 4:

Goal Type .06 .003 .14
W2

STEP 1:

Cognitive Ability -.07 .047 .05
Goal Commitmwt -.03

STEP 2:

Goal Type .06 .007 .05
STEP 3:

MC. Activity (tally) -.15 .027 .08

Leaning Activity (tally) .01
Leanring Activity (score) .02

MC. Activity (score) -.10

MC. Activity (rating) .11

STEP 4: _
Declarative -.08 .056* .14
Procedural -.25*

 

*p<05, **p<.01, n = 106

 

 

 

Hierarchical Regression Results of the Test of Hypotheses 9b — The mediation of the
effects of Goal Type on Inner P.C. Intrusions through Leanring Activity, MC.
Activity, and Knowledge

207

 

VARIABLE B AR2 R2
EQUATIQNJ

STEP 1:

Cognitive Ability .00 .006 .01
Goal Commitmwt .21

STEP 2:

Declarative -.03 .015 .02
Procedural .15

STEP 3:

Learning Activity (tally) .07 .078 .10
MC. Activity (tally) .01

Leaning Activity (score) -.03

MC. Activity (score) -.28*

MC. Activity (rating) -.09

STEP 4:

Goal Type -.04 .001 .10
EQUAIIQNJ

STEP 1:

Cognitive Ability .00 .006 .01
Goal Commitmwt .21

STEP 2:

Goal Type -.04 .001 .01
STEP 3:

MC. Activity (tally) .01 .073 .08
Learning Activity (tally) .07

Leaning Activity (score) -.02

MC. Activity (score) -.28*

MC. Activity (rating) -.09

STEP 4:

Declarative -.02 .021 .10
Procedural .15

 

*p<.05, **p<.01, n = 106

.ull‘ Dan IFS-la