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THE EFFECT OF DATA ACQUISITION-PROBEWARE AND
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REPRESENTATION OF KINETICS IN A HIGH SCHOOL
PHYSICS CLASS

presented by

William Bishop Struck

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THE EFFECT OF DATA ACQUISITION-PROBEWARE AND DIGITAL VIDEO
ANALYSIS ON ACCURATE GRAPHICAL REPRESENTATION OF KINETICS
IN A HIGH SCHOOL PHYSICS CLASS
By

William Bishop Struck

A THESIS

Submitted to
Michigan State University
In partial fulﬁllment of the requirements
for the degree of

Master of Science

Physical Science - Interdepartmental
2007

ABSTRACT
THE EFFECT OF DATA ACQUISITION-PROBEWARE AND DIGITAL VIDEO
ANALYSIS ON ACCURATE GRAPHICAL REPRESENTATION OF KINETICS
IN A HIGH SCHOOL PHYSICS CLASS
By

William B. Struck

The effects of two types of microcomputer-based methods on the ability Of high A
school physics students to accurately graph kinetics using distance, velocity and
acceleration versus time graphs were studied. Student graphing skills were evaluated
before, during and after they used data acquisition-probeware (DAP) and digital video
analysis (DVA) to investigate a variety of one-dimensional motions. Half Of the students,
placed in random groups, first investigated these motions with DAP and later with DVA.
The other half of the students investigated the same motions with the same equipment but
in the reverse order. Both these strategies were found to be successful and
complementary. There were indications student achievement was higher for velocity-

time and acceleration-time graphs using the DVA method.

I dedicate this thesis to the memory of my late parents, Herman and Dorothy Struck.
Without their unwavering love, profound belief in the value of education and boundless

support in countless ways, this work would never have come to pass.

iii

ACKNOWLEDGMENTS

I would ﬁrst like to acknowledge Dr. Randy Yerrick. His inspiration and
encouragement were key factors in my decision to embark on a career in secondary
science education. His generous support of time, expertise in the ﬁeld of science
education, practical suggestions and support were all essential to the completion of this
project. I treasure his friendship and am deeply grateful for his support.

I am appreciative of my advisor, Dr. Merle Heidemann, who has been
wonderfully helpful, positive, friendly, and patient from the ﬁrst day I met her and
throughout the dozen years that I have had the privilege of knowing and working under
her guidance.

I am grateful for my loving wife, Maria Struck, who has been supportive and
patient throughout my numerous career changes and during my recent graduate studies.
Without her encouragement and our love, this thesis would never have been completed.

I would like to thank Dr. Kenneth Nadler for his helpful input into this thesis. I
value the thoughtful discussions we have had about science, education and politics. And
ﬁnally I would like to acknowledge Karen Herdzik from the University of Buffalo and
Dr. Mark Urban-Lurain from Michigan State University for their generous assistance

with the statistical interpretation of the data.

iv

TABLE OF CONTENTS

LIST OF TABLES .................... _ .............................................................. vi
INTRODUCTION ................................................................................... 1
The Call for Reform ........................................................................ 3
Study Site .................................................................................... 9
IMPLEMENTATION .............................................................................. 1 1
Implementation scheme ................................................................... 12
RESULTS ............................................................................................ 17
DISCUSSION ...................................................................................... 28
Future Research ............................................................................ 32
APPENDICES ..................................................... - ................ . .................. 35
REFERENCES......... ............................................................................. 46

LIST OF TABLES

Table 1--T1 to T2, % changes ..................................................................... 18
Table 2--T2 to T3, % changes ..................................................................... 19
Table 3--T1 to T3, % changes ..................................................................... 20
Table 4--Complexity Ratings ...................................................................... 21

vi

INTRODUCTION

AS far back as 1957 when the Soviet Union launched Sputnik, the American
public, its leaders and educators have become increasing concerned over how our science
and math students compare to those from other countries. At that time, the Cold War was
in full swing and the threat was military in nature and national pride was at stake. More
recently the concern has focused on whether or not our students are equipped to compete
in an increasingly global job market. Studies such as National Assessment of
Educational Progress (NAEP) and Third in International Mathematics and Science Study
(TIMSS) are examples of the growing evidence of the shortcomings of United States
students relative to students from other industrialized nations around the world.

The thirty year NAEP study recognized a high interested in “back to the basics” in
reading, mathematics and science. This study found that reading scores for our 17-year-
olds are not signiﬁcantly different from 1971 to 1999. Mathematics scores for 17-year-
olds do Show a Signiﬁcant, though slight (1.3%), increase from 1973 to 1999. However
in science the picture is less encouraging: the overall scores of 17- year-Olds declined
from 1969 to 1982, increased modestly from 1982 to 1992 and have since leveled off.
The end-of-the-study levels (1999) in science for l7-year-olds were signiﬁcantly below
(3.4%) the beginning-of-the-study levels (1969) (Campbell, J .R., Hombo, C.M., and
Mazzeo, J., 2000).

The Third in International Mathematics and Science Study (TIMSS) has studied
and still is studying global trends in math and science education achievement and
curriculum. Its focus is on two groups: 4tln and 8th graders. In the 1999 report on science,

8th grade US students rank signiﬁcantly below fourteen (14) countries, are statistically

even with ﬁve (5) and signiﬁcantly higher than eighteen (18) countries. This “middle of
the pack” picture is not a proud one for our leaders, educators, employers and ordinary
citizens who are concerned with how US students are equipped to compete for
employment opportunities in an increasingly global market (National Center for
Education Statistics).

Recently, additional inﬂuence on the direction of science education comes from
No Child Left Behind Act of 2001. The goal of this legislation is to improve the
performance of our primary and secondary schools through more rigorous standards of
accountability—often called standards-based educational reform. The underlying
principle is that setting lofty goals will improve the performance of all students. One key
component to this legislation is to improve teacher quality by insisting that teachers are
“highly qualiﬁed.” The measure for this focuses on minimally achieving a bachelor’s
degree, teaching only in areas of their major or minor or passing rigorous academic state
tests. Finally, testing achievement is required in science for elementary, middle school
and high school science students.

Some believe that the high percentage of foreign students in the graduate
programs at American universities limits future opportunities for American high school
students entering college. A survey by the National Science Foundation counted the
number of foreign students (as deﬁned as temporary visa holders) and the number of US
citizens and permanent residents for “ﬁrst-time, graduate enrollment.” For the period of
2000-2004, in the physical sciences the percentage of foreign students entering graduate
programs ranged from 39% to 45%. For the same period, in the mathematical sciences

the percentage of foreign students entering graduate programs ranged from 38% to 46%.

(National Science Foundation, 2006). This high percentage of foreign students in US
graduate programs relative to US citizens further emphasizes the rigor of the challenge

that US students face from foreign countries.

The Call for Reform

This study was designed to improve science education by searching for more
effective ways of teaching graphing skills related to a high school kinetics unit. Though
achievement is everyone’s goal in science education, reform continually focuses on
curriculum standards, funding, policies and billions of dollars just for assessment. All of
these seem to drive instruction.

For example The National Research Council compiled a list of data-related

competencies that students should achieve. A portion of this are:

0 Describe and represent relationships with tables, graphs, and rules.
0 Construct, read, and interpret tables, charts, and graphs.
0 Analyze tables and graphs to identify properties and relationships

(NRC, 1996, p.121).

This study was implemented to compare the effectiveness of two different
computer-based technologies in an attempt to improve the ability of high school physics
students to inter-relate various one-dimensional motion Situations with distance-time,
velocity-time and acceleration-time graphs. Half the students in a high school physics
classes used data acquisition probeware (DAP) and then later used digital video analysis
(DVA) to examine one-dimensional motions. The other half of the students in the same

classes used identical equipment to study the same motions but did so in the reverse

order. All students were asked to complete distance-time (d-t), velocity-time (v-t) and
acceleration-time (a-t) graphs for these motions before starting, after using just one
method of study and a third time after using both methods of study. Observing students
during this unit on one-dimensional kinetics unit in a high school offered physics course
provided an excellent opportunity to compare the relative effects of both DAP and DVA
on student kinetic graphing skills.

Traditional educational methods such as teachers lecturing and students dutifully
listening (“I teach; you learn”) have been shown to be seriously lacking. The mid-
twentieth century work of Jean Piaget identiﬁed four distinct stages of cognitive learning:
sensory—motor, preoperational, concrete operational and, by about age 11, the formal
operational stage (Berg & Phillips, 1994). It is no longer accepted that students simply
receive knowledge. Beginning with Piaget our understanding of science learning and
teaching has deepened considerably. Over the past 40 years the work of Lev Vygotsky
and others has seen constructivism become the theoretical basis that educators use to
make sense of science instruction. Constructivism holds that the background and level of
achievement of each learner is the starting point. It then deﬁnes a “zone of proximal
development” which encompasses Skills and concepts that the child is capable of doing
with adult guidance. Within that framework, children develop new skills and revise their
concepts to accommodate new discoveries (Lemke, 2001). In the constructivism theory,
instructors are viewed as facilitators rather than as deliverers of knowledge—teachers
(Furman & Barton, 2006). The learning process is an active social process that calls for
collaboration between the instructor, the learner and the leamer’s peers (Kozma &

Russell, 1997; Gijlers & Jong, 2005). Marcia Linn, who worked with Piaget, is

responsible for the “Scaffolded Knowledge Integration framework” (SKI). Linn holds
that there are four components to science learning. First is the introduction of various
models and creation of an environment that encourages the investigation of these models.
The second component of SKI is what Linn calls “making thinking visible.” This is
essentially the clear encouragement of debating various models. The third component of
SKI is the need to convey to students that this debate is never-ending and is something
true scientists do throughout their lives. The fourth and ﬁnal component is providing the
classroom environment that encourages productive social interaction. Linn’s SKI
evolved into a related framework called “Knowledge Integration Environment” (KIE). A
KIE brings together “technical, cognitive, and social resources to create a productive
electronic learning community.” (Bell, R, Davis, E. A., & Linn, M. C., 1995) From Liu,

Saed Sabah (2007) summarizes Linn’s key points for designing effective science

instruction:
1. “. ..1ink new ideas to what students already know.”
2. “Teachers and technology can help illustrate the complexity of scientiﬁc
thinking and introduce arguments.”
3. “Foster the social interaction in order to let students learn from each
other.”
4. “Creating lifelong learners. In Other words, ‘students need opportunities

for sustained project work. . . ’”
Creation of just such an environment was one of the goals of this study. Small
groups of students brought together different theories about what kinetics graphs should

look like. They were supplied with various technologies (probes, computers, video

cameras and software) to study the one-dimensional motions and put into a small group
environment designed to provide the time and a relaxed atmosphere conducive to peer
learning.

However, remarkably few speciﬁc teaching strategies to improve student
outcomes in science have been proposed and without well-grounded strategies, teachers
may be ill equipped to help their students reach the stated goals (Bowen & Roth, 2005).
To make progress in this theater, direct classroom research aligned to Speciﬁc skills is
needed to investigate the impact particular activities have on achievement. It was the aim
of this study to make just such a contribution by studying speciﬁc technology and related
activities and examine their relationship to student actions.

Computers for educational purposes ﬁrst became available in the 1970’s and
various educational applications have evolved. One of the most enduring uses of
computers is their use for practice, drill and simulations (Adams & Shrum, 1990).
Microcomputer-based laboratories (MBL) were ﬁrst seen in the early 1980’s and are
attractive for a variety of reasons: data acquisition that is quicker, more accurate and in
some cases impossible without MBL (Eisele, 1982). They lend themselves well to the
effective sequence for studying science suggested by Russell, Lucas and McRobbie
(2004): predicting, setting up, observing, analyzing and explaining. Kinetic motion is
especially amenable to study via MBL. By using a sonic motion detector linked to a
microcomputer students can collect and store data for very short time intervals, very
quickly and very accurately. These data can then be displayed in real time as a distance

versus time (d-t), velocity versus time (v-t) or acceleration versus time (a-t) graphs. The

ease and accuracy of gathering the data and generating graphs help make student-lead
investigations more efﬁcient and meaningful (Graef, 1983).

Misconception research has been a major contribution to our understanding of the
constructivist nature of learning. Lillian McDermott believed that students “often have
similar conceptual and learning difﬁculties” and understanding these difﬁculties will
prove helpful in designing effective physics instruction. For example, in -
kinetics/graphing studies students sometimes mistakenly generate graphs as a “picture” of
the event (Linn, M., Layman, J., & Nachmias, R., 1987). A ball rolling down a hill is an
example of positive acceleration but students often draw a velocity vs time graph of this
event with negative slope (deceleration) to look like the hill (Beichner, 1990). This
confusion with velocity-time graphs has been described by Brungardt & Zollman (1995)
as students’ difﬁculty “with the vector nature of physical quantities.” This “graphs as
pictures” phenomenon was also observed by others (Mokros & Tinker, 1987).
Acceleration versus time graphs present similar and perhaps even stronger challenges
because students “have a hard time distinguishing between a quantity [height on a graph]
and a change of that quantity” (Lockhead, 1980).

Students learning to interpret graphs has been a topic of concern. Graphical
presentations are a common form of communication in science, engineering and math .
(Mokros & Tinker, 1987). Articles in scientiﬁc journals frequently use graphs to report
and discuss their results. While high school textbooks have about as many images (non-
text) as scientiﬁc journals, textbooks have far fewer graphs and those they do have lack

rigorous scaling (Bowen, Roth & McGinn, 1999).

Using graphs to study kinetics in a high school physics course provides a good
opportunity for students to construct their own knowledge, have some of their
misconceptions challenged and practice, in a hands-on way, to construct various kinds of
graphs (Mokros & Tinker, 1987). The cognitive development of most students taking
high school physics (16-18 years old) lends itself to the abstract representations that
graphs offer. These students have moved from the concrete operational stage to the
formal operational stage and the others are at least in the process of making that transition
(Mokros, 1986).

One-dimensional kinetics is often one of the ﬁrst topics taught in a high school
physics course. A simple example would be one object moving at a constant velocity.
Higher complexity would be one object starting from different relative positions, still
moving at a constant velocity but in different directions and different velocities. Later
higher complexity motion is studied—one with steadily changing velocity (constant non-
zero acceleration). At each of these levels the complexity can be increased by using more
than one object. Unless the high school physics course is calculus-based, this is as
complex as it gets. Two-dimensional kinetics is studied as a combination of constant
velocity and constant acceleration motion with one object (projectiles). The study of
collisions, both elastic and inelastic, is an extension of constant velocity two—Object
motion with of course the addition of the factors of mass and momentum.

In this study two different methods of data acquisition and study were compared.
Speciﬁcally, the effectiveness of real-time graphical methods using data acquisition
probeware (DAP) was compared to the effectiveness of another method with a signiﬁcant

time delay between the motion/data acquisition and the production of graphs using digital

video analysis (DVA). There are conﬂicting reports about the impacts of such delays.
Brasell (1987) reported that a delay between the motion and appearance of a graph of j ust
20 seconds had a negative impact on learning while others found no such impact
(Beichner, 1990 and Brungardt & Zollman, 1995). A higher level of interest and less
confusion between v-t and a—t graphs has been reported for groups using real-time
graphing techniques like DAP as opposed to delay-time groups like DVA (Escalada and
Zollman, 1997). Both methods were controlled by the students and had students
performing the actual motions, providing important “kinesthetic feedback” (Beichner,

1990).

Study Site

Williamston, Michigan is a town 15 miles east of the Lansing and has a
population of about 3,500 people. Formerly a rural farming community, Williamston has
become more Of a residential town with farming still in the surrounding areas. The
largest employer in the city is Williamston Community Schools (170 employees). Many
of its residents commute to work in nearby Lansing, East Lansing and Okemos (Census
proﬁles). The town is well known for its numerous antique shops. The community
prides itself in maintaining its small, cohesive, friendly town atmosphere. This theme is
reﬂected in the town motto: “Discover the Charm.”

The Williamston Community Schools has four schools: Discovery (K-2),
Explorer (3-5), Middle School (6-8) and High School (9-12). Williamston High School
has a yearly enrollment of approximately 675 students. Williamston schools are not

racially diverse: they are 93.4% white, 1.2% African-American, 3.0% Hispanic, 2.1%

Asian/Paciﬁc Islander and 0.3% Native-Americans. The average family income in
Williamston is $51,000. Only 7.0% of the students are classiﬁed as economically
disadvantaged compared to 35.2% for the State of Michigan as a whole (School Matters).

Students who take physics at Williamston High School are required to ﬁrst take
Biology and Physical Science and at least Advanced Algebra. Most have also completed
Chemistry and either Functions, Statistics and Trigonometry (F ST) or Pre-Calculus.
Most students taking physics have a strong interest in science and many, go on to major in
a science in college. The majority of the physics students are relatively strong
academically—it’s unusual for the valedictorians n_o_t_ to have taken physics. Some
students are capable of being successful in physics but don’t keep pace with the rigorous
homework and lab demands. Each year a few students enroll against the advice of their
counselor and/or previous science teachers. Due to weak math skills and weak study
habits, a signiﬁcant percentage of these drop the course by the end of the 1St semester.
Thirty-nine of the 44 enrolled students completed the study (89%). The ﬁve students
who did not complete the study were absent during a Signiﬁcant number of activities and
got out of the test/activity sequence. This kinetics unit was early in the ﬁrst semester so
no students had yet dropped the course. By the end of the year, 5 students had dropped
the class—1 1% of the original enrollment. So it is reasonable to say that the students
who completed this study represent a typical group of Williamston students taking

physics in the early fall.

10

IMPLEMENTATION

In this study, students used two methods to investigate one object moving in 1
dimension and two objects moving in 1 dimension. One method required sonic motion
detectors linked to the PASCO Xplorer GLX hand-held computers. This method was
called digital acquisition probeware (DAP). The second method was called digital video
analysis (DVA). It required a low resolution digital video camera (Canon), LoggerPro
3.0 software from Vernier and desktop computers belonging to the students or in the
school’s computer labs. These methods were chosen for the study because both use
technology now readily available for high school physics classes.

Prior to beginning their investigation, students were given a test (T1) to evaluate
their initial abilities at graphing motion. (See appendices l and 2.) During this test they
were given eleven (1 l) motion circumstances and asked to sketch three graphs for each:
1) distance vs time, 2) velocity vs time and 3) acceleration vs time. For each motion
situation, students were also asked to rate the complexity of the problem using a 1-5
scale. One week later students were randomly placed in groups of three to study the
eleven motion situations. Half the groups had sonic motion detectors linked to hand-held
graphing computers (DAP) while the other groups took digital movies and created graphs
on personal computers using digital video analysis software (DVA). Two days later
students were then retested (T2) on the same set of eleven motion situations. Two days
later groups switched equipment to reexamine the same motion situations. Three days
later students took the evaluation test for a third and ﬁnal time (T3). The total duration of
the study was 15 days. This sequence is summarized on the following ﬂow chart entitled

“Implementation Scheme.”

11

Implementation Scheme
Three tests were taken (T1, T2 and T3)

Each test described eleven motion circumstances.
For each motion, students were asked to sketch three graphs: distance-time, velocity-time
and acceleration-time (see appendix 1).
For each motion, students were also asked to rate how they perceived the complexity of
the motion circumstance: least complex to most complex on a 1-5 scale.
Between tests, students examined the eleven motion situations using two methods:
Digital acquisition probeware (DAP) from PASCO
Digital video analysis (DVA) from Vernier
Group 1 (DVA followed by DAP)
T1 9 seven days 9 DVA 9 two days 9 T2 9 two days 9 DAP 9 three days 9 T3
Group 2 (DAP followed by DVA)

T1 9 seven days 9 DAP 9 two days 9 T2 9 two days 9 DVA 9 three days 9 T3

12

The interaction of students during these exercises was observed and the pre, mid
and post graphing sketches (T1, T2, and T3, respectively) were scored using a common
rubric. Results were evaluated graphically and statistically.

Each sonic motion detectors was linked to a hand-held computer. With data
acquisition-probeware (DAP) that allowed real-time data capture and graphical display.
PASCO sonic detectors are sensitive in a 15-800 cm range and have sampling rates of
0.020 seconds. Students were instructed to use themselves as the moving object. Since
sonic motion detectors cannot distinguish between different objects, multiple object
circumstances were studied one object at a time. During each motion, by using the
computer’s keyboard and display, students were able to immediately view plots of
distance-time, velocity-time and acceleration-time. DAP allows students to construct
their own experiment, be directly involved kinesthetically and see real-time d-t, v-t and a-
t graphs of motion events.

The digital video analysis method (DVA) required two steps. First a low
resolution digital video was taken of each motion and the data were captured on a 3 1/2"
ﬂoppy disk. Once again students used themselves as the moving object. Because of the
low resolution and brevity of each movie (usually 5 seconds or less), the ﬁle size was
small and easily conveyed to each member of the group by email or by copying the ﬁle to
a ﬂoppy disk. Each student subsequently used LoggerPrO 3.0 (Vernier) to analyze the
ﬁlmed motion and eventually developed distance-time, velocity-time and acceleration—
time graphs for each motion. This process required a PC. When each video ﬁle was
opened with LoggerPro two scaling tasks were required: 1) an origin (known as

“homebase”) was established with two mouse clicks and 2) a distance soale was

13

established by clicking and dragging on a meter stick that was visible in each video.
With those preliminary steps out of the way then the motion was digitally traced by
placing the cursor on the object. A click of the mouse recorded the position of the object
and advanced the movie by one frame—equivalent to 1/30th of a second. Once the video
analysis was completed, distance-time, velocity-time and acceleration-time graphs were
easily viewed on the computer screen and/or printed out. Best-ﬁt lines were also easily
generated and viewed. The shape and SIOpe of these best-ﬁt lines on a distance-time
graph helped students view the magnitude and Sign of velocity. Similarly, the shape and
slope of these best—ﬁt lines on a velocity-time graph helped students comprehend the
magnitude and sign of acceleration. DVA does not produce real—time graphs but does
require students to construct the activity, actually perform the motion, analyze the video
data and ﬁnally view the graphical results.

These two methods of motion evaluation had some commonalities and several
: distinct differences. Both methods required close group cooperation during the data
collection. Instructions were read together and students had to agree on what the
instructions meant. Then group members divided up the jobs: one or more members
would perform the motion while another recorded data by starting the sonic
motion/computer or taking the digital movie. Whether or not the captured data were
within acceptable limits could easily and quickly be determined by both methods of
study. The DAP had a screen that gave a real-time graphical display of the event and the
DVA allowed for an immediate review of the captured movie. Groups would frequently

repeat a motion until “clean” data were captured for study.

14

Using the DAP method, motion graphs were studied immediately through the on
screen display features. Distance vs time data were the default mode of display. Some
modest-level computer skills were required to bring up the velocity vs time and
acceleration vs time displays. Typically, at least one group member quickly ﬁgured out
how to do that and either taught the other group members or simply performed the
necessary key strokes after each motion and showed his group members the three
different graphs. Most groups started the sonic detector and data collection before the
motion. This created a brief “acceleration spike” as the moving person went from
motionless to some constant non-zero speed. Students quickly accepted these spikes as
“start-up anomalies.” Multi-object motions had to be studied with separate runs. Motion
was always relative to the detector: motion could be going either away from the detector
(giving a positive slope on a distance vs time graph) or toward the detector (giving a
negative slope on a distance vs time graph). Distances from the detector would always be
displayed as positive (Me the x-axis).

The DVA method differed somewhat from the DAP. Though the initial video
required group cooperation, the analysis of each video take was done separately by each
individual, at a later time and usually outside of the classroom. The DVA method
required more time overall: slightly less classroom time but considerably much more time
Outside the classroom and in front of a computer. The analysis was considerably delayed
from the initial time of the ﬁlming. However, the motion studied could be reviewed as
many times as desired Simply by replaying the video. Multiple Object motions were

ﬁlmed simultaneously and their respective motion graphs could be also be viewed

15

simultaneously. Since the origin (homebase) could be set anywhere on the screen, both
positive as well as negative distances could be graphed.

Once students sketched graphical representations of the various motion situations,
their sketches were scored and averages were calculated. These average scores Of actual
student performance were themselves graphed as part of the analysis for this project.
These result graphs were then compared to graphs of several hypothetical educational
outcomes. The hypothetical outcome graphs took into account two factors:

1) the pre-study skill level of each group

2) the amount of improvement after each method of study.

Pre-study skill levels of each group (groups “A” and “8”) had three possibilities: A = B,
A > B, and A < B. The amount of improvement after each method of study (“C” and
“D”) also had three possibilities: C = D, C > D and C < D. This lead to the construction
of ﬁve hypothetical graphs (appendices 5 to 9):

Hypothetical #1 Pre-study skill level: A = B

Amount of improvement: C = D
Hypothetical #2 Pre-study skill level: A 75 B
Amount of improvement: C = D
Hypothetical #3 Pre-study skill level: A = B
Amount of improvement: C > D
Hypothetical #4 Pre-study skill level: A < B
Amount of improvement: C > D
Hypothetical #5 Pre-study skill level: A > B

Amount of improvement: C > D

16

Results

Eleven motion situations were studied. Thirty-nine students each sketched a d-t,
v-t and a-t graph for these eleven Situations at three different times: T1, T2 and T3. In
all, 3,861 graphs were sketched by students. These student-generated graphs were blindly
scored, recorded and statistically compared. The statistical evaluation of graph score
changes was made using the General Linear Model and a P value of S 0.05 for 95%
reliability. Improvement trends from pre-investigation levels to mid-investigation levels
(T1 to T2), from mid-investigation to ending levels (T2 to T3) and from pre-investigation
levels to ending levels (T1 to T3) were examined for all graphs (d-t, v-t and a-t) and for a
composite score (the sum of d-t, v-t and a-t scores). A Pearson Correlation Coefﬁcient (P
5 0.05) was generated to relate students’ perception of each motion’s complexity to their
composite score for each of the eleven motion circumstances. These results are
summarized in appendix G. Any differences between the two methods of learning (DAP
and DVA) were also evaluated. Beginning scores (Tl), mid-point scores (T2) and ﬁnal
scores (T3) were statistically compared. These results are summarized in Tables 1-4.
The scores were also graphed and compared to hypothetical graphing patterns

(appendices 5-9).

17

T1 to T2, % changes

Table 1

 

 

 

 

 

 

 

 

B C D E F G H I J K
d-t +9.8 +21 +12% +9.2 +16 +19 +19
v-t +9.7 +22 +23 +25 +26 +55 +54
a—t +13 +18 +27
composite +11 +17 +12 +13 +9.3 +14 +28 +31

 

 

 

 

 

 

 

 

 

 

Each column, A-K, represents one the eleven motions studied (appendix 1).

The ﬁrst three rows contain the improvement of the average scores for the
distance-time, velocity-time and acceleration-time sketches from the initial test
(T1) to the mid-study test (T2).

The bottom row contains the improvement of the average of the total scores for all
three is the sketches for the same time period.

All posted results were statistically signiﬁcant (General Linear Model, P S 0.05).
Blank boxes indicate that there was no signiﬁcant change.

Bold indicates that group 1 was statistically stronger than group 2.

l8

 

Table 2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

T2 to T3, % changes
A B C D E F G H I J K
d-t
v-t +16 +16 +15
a-t +9.7 +8.8 +11 +9.0 +11 +9.3
composite +4.0 +4.8 +7.0 +5.2 +7.8 +7.0 +6.1 +6.4 +7.8 +8.1

0 Each column, A-K, represents one the eleven motions studied (appendix 1).

o The ﬁrst three rows contain the improvement of the average scores for the
distance-time, velocity-time and acceleration-time sketches from the mid-study
test(T2) to the ﬁnal test (T3).

0 The bottom row contains the improvement of the average of the total scores for all
three is the sketches for the same time period.

0 All posted results were statistically signiﬁcant (General Linear Model, P S 0.05).

0 Blank boxes indicate that there was no signiﬁcant change.

0 Bold indicates that group 1 was statistically stronger than group 2.

19

 

Table 3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

T1 to T3, % changes
B C D E F G H I J K
d-t +11 +22 +13 +9.2 +18 +19 +20
v-t +12 +28 +36 +34 +27 +36 +27 +38 +80 +78
a-t +13 +12 +19 +11 +11 +12 +22 +25 +29 +39
composite +8.5 +16 +25 +18 +16 +21 +16 +22 +37 +42

 

 

 

 

 

Each column, A-K, represents one the eleven motions studied (appendix 1).

The ﬁrst three rows contain the improvement of the average scores for the
distance-time, velocity—time and acceleration-time sketches from the initial test
(T1) to the ﬁnal test (T3).

The bottom row contains the improvement of the average of the total scores for all
three is the sketches for the same time period.

All posted results were statistically signiﬁcant (General Linear Model, P S 0.05).
Blank boxes indicate that there was no signiﬁcant change.

Bold indicates that group 1 was statistically stronger than group 2.

20

Table 4

 

 

 

 

 

Complexity Ratings
B C D E F G H I J K
T1 2.1 2.3 2.7 2.7 2.8 2.8 3.1 3.0 3.4 3.9
T2 1.6 1.6 2.0 2.1 2.1 2.2 2.6 2.6 3.0 3.4
T3 1.4 1.6 1.8 2.0 2.0 2.0 2.5 2.5 2.7 2.9

 

 

 

 

 

 

 

 

 

 

 

 

 

Each column, A-K, represents one the eleven motions studied (appendix 1).

The three rows contain the average of the complexity scores that students gave for
each motion during the initial test (Tl), mid-study test (T2)'and the ﬁnal test (T3).
The scale was from 1 (least complex) to 5 (most complex).

Bold indicates a positive correlation between the complexity score with

composite score (Pearson Correlation Coefﬁcient , P S 0.05).

21

Motion A: An object starts at homebase, is initially motionless and remains
motionless.

This was the simplest motion circumstance—no motion. Students had very high
competency scores on all three graphs on all three tests (Tl , T2 and T3)--in the 95-100%
range. They also rated this scenario as the least complex of the 11 motion circumstances:
1.3 (on a 1-5 scale) at T1 and 1.1 at T3. No signiﬁcant improvement was measured for
any graph for either group at any stage.

Motion B: An object starts at homebase and moves steadily to the right.

During the T1 testing, students scored very high on their distance vs time graphs
(98%) but less well on their graphs of velocity vs time (81%) and acceleration vs time
(83%). The most common error was a v-t graph with a positive slope starting from the
origin rather than a horizontal zero-slope line above the x-axis. No improvement in d-t
graphs was found at any stage. Velocity-time graphs improved to ~90% from T1 to T2.
Group 1, the group ﬁrst using digit ’video analysis (DVA), showed signiﬁcantly higher
gains than group 2, the group ﬁrst using digital acquisition probeware (DAP). No
additional improvement was found in v-t graphs from T2 to T3. Acceleration-time
graphs showed no improvement until T2 to T3 and group 1 again showed higher gains.
Distance-time graphs scores were 98.3% at T1 and 100% at T3--no signiﬁcant overall
improvement. Overall improvement (from T1 to T3) was found for both v-t and a-t
graphs with group 1 showing stronger gains in the v-t graph but no group differences
were seen in the a-t graphs. The complexity of the circumstance was 2.0 at T1 and
steadily decreased to 1.4 by T-3. No correlation was found between student complexity

perceptions and composite scores.

22

Motion C: An object starts at homebase and moves steadily to the left. '

This motion was identical to the previous circumstance in starting place, number
of objects and constant velocity. The only difference was the direction of movement: to
the left of homebase instead of to the right. Distance-time graphs at T1 were lower than
previous scores (81%) and rose by 9.8% from T1 to T2 but not signiﬁcantly after that.
Velocity-time graphs improved even more dramatically (22%) frOm T1 to T2 but again
not from T2 to T3. Acceleration-time graphs did not improve from T1 to T2 but rose. by
12% from T2 to T3 with Group 2, using DVA, showing signiﬁcantly higher gains.
Overall improvement was measured for all three time periods for all three graphs with no
group differences. Complexity perception scores diminished from 2.3 at T1 to 1.6 at T2
and T3. There was a positive correlation between students’ complexity perception and
there composite score at T3.

. Motion D: An object starts to the right of homebase and moves steadily to the left.

This is the ﬁrst of the motions to be analyzed that does not start at homebase.
However the motion brings it closer to or even past homebase. Both groups of students
recognized this motion as more complex and challenging than the ﬁrst three. The d-t
scores at T1 were the lowest average yet: 81%. This was primarily due'to the
misplacement of the object’s beginning position. From T1 to T2, dramatic improvement
of d-t graphs (21%) and v-t graphs (23%) was shown; however, neither d-t nor v-t graphs
improved from T2 to T3. Acceleration-time graphs improved (11%) from T2 to T3. All
three graphs show signiﬁcant overall improvement (T1 to T3). No group differences

were seen in any graph at any stage. The level of student conﬁdence improved from a T1

23

score of 2.7 to a T3 score of 1.8. Positive correlations between this complexity
perception and composite scores at T1 and T3 were found.
Motion E: An object starts to the left of homebase and moves steadily to the right.
This motion is a mirror image of the previous motion so one might expect similar
scores and trends. Interestingly, the conﬁdence level was not as high as the previous
motion but the performance was nearly identical. Signiﬁcant improvement of d-t graphs
(up 12% to 98%) and v-t graphs (up 23% to 80%) was shown during T1 to T2. No
signiﬁcant difference in d—t and v-t graphs was seen from T2 to T3. N0 improvement
was seen for a-t graphs from either T1 to T2 or T2 to T3. Overall improvement was seen
in all three graphs. No group differences were seen in any graph at any stage. The level
of student conﬁdence improved from a T1 score of 2.7 to a T3 score of 2.0. Positive
correlations between complexity perception and composite scores at T2 and T3 were
found.
Motion F: An object starts to the right of homebase and moves steadily to the right.
This motion is unidirectional: the object starts and moves right. Perhaps because
Of the similarity with the previous motion circumstances, students showed improvement
of d-t graphs from T1 to T2 only: from 89% to 97%. Velocity-time graphs improved
from T2 to T3 but not from T1 to T2. Similarly, acceleration-time graphs improved from
T2 to T3 but not from T1 to T2. Overall improvement was seen in all three graphs. No
group differences were seen in any graph at any stage. The level of student conﬁdence
improved from a T1 score of 2.8 to a T3 score of 2.0. Positive correlations between
complexity perception and composite scores at T2 and T3 were again found.

Motion G: An object starts to the left of homebase and moves steadily to the left.

24

Like the previous motion, this was unidirectional: the object starts and moves left.
Students showed improvement of d-t graphs from T1 to T2 only: from 82% to 95%.
Students showed no signiﬁcant improvement in their v-t graphs from either T1 to T2 or
T2 to T3. Students also showed no signiﬁcant improvement in their a-t graphs from
either T1 to T2 or T2 to T3. Overall improvement was seen in all three graphs. No
group differences were seen in any graph at any stage. The level of student conﬁdence
improved from a T1 score of 2.8 to a T3 score of 2.0. Positive correlations between
complexity perception and composite scores at T2 and T3 were once again found.
Motion H: Two objects start at homebase and both move steadily to the right.

The black object moves faster than the blue object.

This is ﬁrst time students had been introduced to 2-object motion and different
velocities. The objects were moving in the same direction, at different but constant
speeds. These two new variables pushed student complexity perception ratings to their
highest levels (3.1). Distance-time graphs started high (96%) and ﬁnished even higher
(99%) and showed no signiﬁcant improvement during either T1 to T2 or T2 to T3.
Velocity-time graphs also showed modest upward trends (71% to 82% to 90%) but were
not signiﬁcant for either T1 to T2 or T2 to T3. Acceleration-time graphs showed
improvement from T1 to T2 but not during T2 to T3. Overall improvement was seen in
v-t and a-t graphs but not in d—t graphs. No group differences were seen in any graph at
any stage. The level of student conﬁdence improved from a T1 score of 3.1 to a T3 score
of 2.5. A positive correlation between complexity perception and composite scores at T3

was found.

25

Motion I: Two objects start at homebase and both move steadily and at the same speed.
The blue object moves to the left and the black object moves to the right.

This motion differed from the previous one (Motion H) because the objects move
in different directions at the same speed. Students perceived this as the same complexity
level as Motion H. Distance-time graphs once again started high (92%) and ﬁnished
even higher (99%) and showed no signiﬁcant improvement during either T1 to T2 or T2
to T3. Velocity-time graphs improved markedly (26%) from T1 to T2 but not from T2 to
T3. Acceleration-time graphs did not improve from T1 to T2 but rose by 11% from T2 to
T3 with Group 2, using DVA, showing signiﬁcantly higher gains. Overall improvement
was again seen in v-t and a-t graphs but not in d-t graphs. No group differences were
seen in any graph at any stage except for the a-t graphs from T2 to T3. The level of
student conﬁdence improved from a T1 score of 3.0 to a T3 score of 25. Positive
correlations between complexity perception and composite scores at T2 and T3 were
once again found.

Motion J: Two objects start in the same position to the right homebase.
Both objects move steadily toward homebase—the black object moves faster
than the blue object.

This motion has the same starting position and directions of movement but
different speeds. Students showed gains with all graphs at all times except with d-t
graphs from T2 to T3. The highest overall gains were shown in v-t graphs (80%)
compared to a-t graphs (29%) and d-t graphs (19%). No group differences were seen in

any graph at any stage. The level of student conﬁdence improved from a T1 score of 3.4

26

tO 3.0 at T2 to 2.7 at T3. Positive correlations between complexity perception and

composite scores at T2 and T3 were once again found.

Motion K: A black object starts 1.0 m to the left of homebase and the blue object starts
2.0 m to the right. Both move steadily toward homebase. The blue is the
ﬁrst to arrive at homebase.

This motion was the most challenging of all for students to analyze. Both objects
started away from homebase, one to the left and one to the right and at different
distances. Both objects moved in different directions at different speeds. Not
surprisingly students rated this motion complexity higher than any other at both the
beginning (3.9) and at the end (2.9). Students made strong gains in all their graphs from
T1 to T2: 19% in d-t graphs, 54% in v-t graphs and 27% in a-t graphs. From T2 to T3,
students continued to show improvement for v-t graphs (15%) but no additional in their
d-t and a-t graphs. Overall improvement was seen in all three graphs. No group
differences were seen in any graph at any stage. The level of student conﬁdence
improved from 3.9 at T1 to 3.4 at T2 to 2.9 at T3. Positive correlations between

complexity perception and composite scores at T2 and T3 were once again found.

27

DISCUSSION

The combined use of digital video analysis (DVA) and data acquisition probeware
(DAP) by high school physics students consistently produced signiﬁcant overall
improvement in their graphing skills. In a few cases, after studying motion using DVA,
students made more improvement producing v-t and a-t graphs than after using DAP.
Based on self-ratings, for all eleven motion situations studied and at every stage of
investigation (after using either DVA and DAP) students became increasingly
comfortable with their motion graphing skills.

Overall improvement of student skills for graphical representation of motion
situations isn’t surprising after the activities used for this study. Students spent over a
week studying the relationship between these motions and how they should be
represented on d-t, v-t and a-t graphs. Students also constructed their own experiment
and kinesthetically experienced the motions and either immediately saw those motions
graphed (DAP) or created their own graphs by gleaning position and time measurement
from digital videos using video digital analysis software. During each motion, by using
the computer’s keyboard and display, students were able to immediately view plots of
distance-time, velocity-time and acceleration-time. Several researchers have reported
that the combination of these factors gives the best cognitive results (Brasell, 1987;
Mokros & Tinker, 1987; Beichner, 1990). These activities had students personally
involved in actual motion events and some other researchers have reported that viewing a
realistic event may be a more important factor for student learning than the real-time
factor (Brungardt and Zollman, 1995). Since students took videos of their own motion,

these events when viewed later on a computer certainly qualify as realistic.

28

Distance-time graphing skills increased to high levels independent of which
method of study was used by students. No improvement was seen only when high levels
(>90%) had already been achieved. In several cases (motions A, B, H and 1) these high
levels were already present before the unit began (T1). In all other cases (C, D, E, F, G, J
and K), high levels d-t graphing skills were quickly achieved (by T2) regardless of
method of study (DVA or DAP).

However, students’ velocity-time graph scores were consistently lower than their
scores for d-t graphs. This isn’t surprising since some students see graphs as pictures
(Linn, M., Layman, J ., & Nachmias, R., 1987). A d—t graph comes the closest to a
picture. Further up or down the y-axis (distance) is further away. Above the horizontal
axis represents one direction while below it represents the opposite direction. But v-t
represents a rate of movement and an a-t graph represents a rate of change of movement.
Both are a long way from being pictures (Brungardt & Zollman, 1995). Velocity-time
and a—t graphs require students to reason beyond the concrete stage--the formal
operational stage.

In this study, this progression of difﬁculty was seen. As mentioned earlier, d-t
graphs were drawn with a high degree of skill with one level of study with either method
or, in a third of the cases, with no study at all. Velocity-time graphing skills began at a
lower level, progressed signiﬁcantly using just one study method (by T2) in 70% of the
cases. Unlike d-t graphing skills, v-t graphing skills continued to improve in some cases
(30%) through the use of the second method of study. Overall improvement of v-t
graphing skills was seen in all but the simplest, already well-understood, circumstance.

Improvement ranged from 12% in a simple case (B) to about 80% in the most complex

29

cases (.I & K). One case (B) indicated that perhaps DVA was a more powerful method
but it’s difﬁcult to explain how seeing this effect in only 1 out of 10 situations signiﬁes a
trend.

Acceleration-time graphs once again showed how one more level of complexity
challenged students. Both initial and ﬁnal scores in student a—t graphs were lower than
their v-t scores. And improvement was harder to achieve despite the ample room “at the
top.” In only 30% of the cases (ignoring A—the motionless situation) was improvement
demonstrated after using just one method (H, .I and K). Sixty percent of the motion
situations (B, C, D, F, I and J) showed improvement after using a second method of study
(T2 to T3). Overall improvement for v-t and a-t graphs was seen for all 10 motion
situations but average a-t gains were modest (19%) when compared to average gains for
v-t graphs (40%). This modest improvement in a—t graphing skills may be due to several
factors: 1) an a—t graph is one more level away from concrete thinking therefore a tougher
concept for many students (Lockhead, 1980), 2) observing an a-t graph using DAP
requires more key strokes than d-t and v-t graphs and 3) generating an a-t graph using
DVA likewise requires more data manipulation before it can be viewed. Any
combination of these challenges makes drawing correct a-t graphs a more daunting task.
There were three cases in T2 to T3 where DVA may have lead to higher scores. But it
should be noted that these students had used DVA m using DAP. If DVA were a big
advantage to understanding a—t graphs, one might expect to see these differences in
students using DVA befo_re_ using DAP. But this was not the case.

The correlation between high complexity perception scores and high composite

scores is interesting. This may show that students less skilled with the more challenging

30

v-t and a—t graphs are not even aware of the challenges. Conversely those students who
are more aware of how challenging v-t and a—t graphs are, especially for the more
complex motions (D-K), Were better prepared to correctly sketch them.

It may be that the use of both DVA and DAP is beneﬁcial and complementary.
Having both methods available is probably a luxury few high schools can afford. At one
level, the time delay between the actual motion and appearance ofa graph (when using
DVA) did not seem to affect overall student learning. However, there may be at least a
second level to consider. The potential damaging effect of this delay, if there is one,
could be offset or more than offset by two advantages of DVA: a) the opportunity that it
affords students the opportunity to repeatedly “relive” the motion by replaying the video
and 2),simply spending more time on task—analyzing the videos took a signiﬁcant
amount of out-of-class time. Conﬂicting reports about the effect of time delay have been
previously reported (Brasell, 1987; Beichner, 1990 and Brungardt & Zollman, 1995).
Most students and some teachers would see this extra time required by DVA as a
negative. Indeed, due to time constraints, the plan for students to study and graph more
complex motion circumstances (e. g. constant but non-zero acceleration) was abandoned

(appendices 3 and 4).

31

Future Research

With more time it would be interesting to study results using the more complex
motion scenarios (appendices 3 and 4). These are more challenging situations because
they describe constant non-zero acceleration situations, motions of multiple objects at
unequal Speeds and have stop-start motions. Research comparing the effectiveness of
DVA and DAP for these situations would be interesting. The ability of DVA to
simultaneously analyze multiple objects and later present them on a single graph might
Show stronger student gains in those types of circumstances.

Doing a similar study with a larger population (this study involved only 39
students) might produce more signiﬁcant results. In this study, graphs Of some of the
results showed patterns similar to hypothetical graphs for unequal method effects but
these effects were not statistically signiﬁcant.

It would also be interesting to study motions of Objects that were not students to
see if their physical participation would make a signiﬁcant difference. It would be
relatively easy to use basketballs or motion carts for non-human motion. Both DAP and
DVA adapt easily to those objects. A study comparing a group using human motion to a
group using non-human motion might be able to measure whether or not direct student
involvement makes a difference in the improvement of their graphing skills.

Longer and higher resolution videos that produce larger ﬁles are now more
practical due to widespread use of jump drives. Students can now easily transport very
large ﬁles home or to school computer labs and study them with digital video analysis
software without having to send them through a high speed intemet connection. This

makes the use of DVA easier and applicable to more complex motions requiring longer

32

duration videos. Higher resolution digital movies make the study of small objects motion
(golf balls and air pucks are examples) more easily accomplished using DVA. Sonic
motion detectors (used in DAP) may not be sensitive enough for such studies. The effect

of object size on student improvement‘would be an interesting topic for future research.

33

APPENDICES

34

Appendix 1

Motion Evaluation I
Conventions & Reminders:

l) The origin represents “homebase.”

2) When observed from a spot perpendicular to the original direction of motion,
positions to the right of homebase are considered positive.

3) For each motion described below, carefully sketch three graphs:
0 distance vs time
o velocity vs time
o acceleration vs time

4) For each motion rate how difﬁcult it was for you to sketch the appropriate
graphs. One (1) is for “very easy” and ﬁve (5) is for “very difﬁcult.”

Motions:
A) An object starts at homebase, is initially motionless and remains motionless.

B) An object starts at homebase and moves steadily to the right.

C) An object starts at homebase and moves steadily to the left.

D) An object starts to the right of homebase and moves steadily to the left.
E) An object starts to the lef t of homebase and moves steadily to the right.
F) An object starts to the right of homebase and moves steadily to the right.
G) An object starts to the left of homebase and moves steadily to the left.

H) Two objects start at homebase and both move steadily to the right. The black
object moves faster than the blue object.

1) Two objects start at homebase and both move steadily and at the same speed.
The blue object moves to the left and the black object movesto the right.

J) Two objects start in the same position to the right homebase. Both objects
. move steadily toward homebase—the black object moves faster than the blue
object.

K) A black object starts 1.0 m to the left of homebase and the blue object starts
2.0 m to the right. Both move steadily toward homebase. The blue is the ﬁrst
to arrive at homebase. ' ‘

35

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

m 522:?

hoe E .30 E
has
a;
. «5.5%
583.2%
3G0 §@ 30

 

 

 

36

Appendix 3

Motion EvaLIatiorLLI
Conventions & Reminders:

l) The origin represents “homebase.”

2) When observed from a spot perpendicular to the original direction of motion,
positions to the right of homebase are considered positive.

3) For each motion described below, carefully sketch three graphs:
0 distance vs time
o velocity vs time
o acceleration vs time

4) For each motion rate how difﬁcult it was for you to sketch the appropriate
graphs. One (1) is for “very easy” and ﬁve (5) is for “very difﬁcult.”

Motions:

A) An object starts at homebase, is initially motionless and experiences constant positive
acceleration.

B) An object starts at homebase, is initially motionless and experiences constant negative
acceleration.

C) An Object starts to the right of homebase, is initially motionless and experiences
constant negative acceleration.

D) An object starts to the left of homebase, is initially motionless and experiences
constant positive acceleration.

E) An object starts to the right of homebase, is initially moving to the right experiences
constant negative acceleration.

F) An object starts to the left of homebase, is initially moving to the left and experiences
constant positive acceleration.

G) Two objects start at homebase, both are initially motionless and both experience
constant positive acceleration. The red object’s acceleration is greater than the green
object’s.

H) A red object starts to the right of homebase while the green object Starts at

homebase. Both are initially motionless and both object experience the same
negative constant acceleration.

37

(Appendix 3, continued)

1) Two objects start at homebase. Both are initially moving to the right—the green

object’s initial motion is faster than the red object’s. Both experience identical
constant negative acceleration.

J) A green object is to the right of homebase a red object is twice as far to the left of
homebase. The green object experiences negative acceleration and the red object

experiences positive acceleration. The magnitude of the red object’s acceleration is
triple that of the green object.

38

Appendix 4
Motion Evaluation 111

Conventions & Reminders:
1) The origin represents “homebase.”

2) When observed from a spot perpendicular to the original direction of motion,
positions to the right of homebase are considered positive.

3) For each motion described below, carefully sketch three graphs:
0 distance vs time
o velocity vs time
o acceleration vs time

4) For each motion rate how difﬁcult it was for you to sketch the appropriate
graphs. One (1) is for “very easy” and ﬁve (5) is for “very difﬁcult.”

Motions:

A) An object starts at homebase, slowly and steadily moves to the right, stops for second
and then moves quickly and steadily to the right.

B) An object starts at homebase, and steadily moves to the left, stops for 2 seconds and

then moves steadily to the right. The speed of the left movement is equal to the speed
of the right movement.

C) An object starts to the left of homebase, slowly and steadily moves to the left and
then immediately moves quickly and steadily to the right.

D) An object starts to the left of homebase, moves steadily to the right, stops for 1
second and then accelerates steadily to the left.

E) An object starts to the right of homebase, is initially moving to the left and accelerates
steadily to the right.

F) An Object starts to the left of homebase, steadily accelerates to the right and then
moves steadily to the right.

39

Appendix 5

hypothetical #1; equal groups; DVA = DAP, both +

IUD—l

I

d

DAP ->DVA on
CD
0
l

 

DVA --> DAP (%)

Y?-

l—. .:DVA..> DAPl
l [.4 .:DAP ~>DVA

 

 

AD—T I I r 1 T I l T T T I 1 I .._.._'__ I T T I T l I ”‘

Test (#)
(1.745, 60.4)

 

40

 

Appendix 6

hypothetical E-2; A < B; DVA = DAP

DAP ->D\/A (%)

 

 

DVA --> DAP (%)

a...” .2DVA ..> DAPE
.:DAP -->D\/A

 

 

AOﬁ I I I I I I I I I I I I I I I T I I I I I

(1.567. 55.1)

 

 

 

 

41

DVA -> DAP 1%) DAP ->DVA (%)

Appendix 7

hypothetical #3; A = B; DAP < DVA

 

 

xi
.:DVA --> DAP;
l: .:DAP -->DVA

1...... . -._.. W.

    

 

(1.553, 51.52)

 

42

 

Appendix 8

hypothetical #4; A < B; DVA > DAP, both +

105+;

DAP ->DVA (%)

 

DVA --> DAP 1%)

 

 
 

H“ .:DVA --> DAPI
H .:DAP -->DVA

 

40% I l T I I I I I I I I I I I I I I I l I I
1.0 1.5 2.0 2.5 3.0
Test (#3

 

 

 

 

(1792.52.11)

 

 

 

43

Appendix 9

hypothetical #5; A > B; DVA 2» DAP, both positive

Ion—g ,

 

 

 

 

 

50—?

DVA -> DAP (%) DAP ->DVA 1%)

55—i

 

 

11:155.: .92.: .5?)

 

 

 

 

44

REFERENCES

45

Adams, D.D. & Shrum, J.W. (1990). The Effects of Microcomputer-based Laboratory
Exercises on the Acquisition of Line Graph Construction and Interpretation Skills by
High School Biology Students. Journal of Research in Science Teaching, 22(8), 777-
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