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Smith has been accepted towards fulfillment of the requirements for M.A. Psychology degree in @421 f; /r ,, L/ Major professor 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution LIBRARY Michigan State Unlverslty PLAcE DI RETURN ”Xbmmwmfiunywrm TOAVOIDFINESrmnonorbdonddodm. DATE DUE DATE DUE DATE DUE MSU ION! mm Action/EM Opponunly lmlulon 1 THE EFFECTS OF ADVANCE ORGANIZERS AND DIFFERENT METHODS OF SYMBOLIC CODING ON TRAINING OUTCOMES By Matthew Ross Smith A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF ARTS Department of Psychology 1994 ABSTRACT THE EFFECTS OF ADVANCE ORGANIZERS AND DIFFERENT METHODS OF SYMBOLIC CODING ON TRAINING OUTCOMES BY Matthew Ross Smith Using an information processing perspective, this research was designed to investigate the linkage between previous knowledge and new learning on training outcomes by examining three variables: advance organizers, symbolic coding, and cognitive ability. The research was broken down into two studies. In the first study, the effects of advance organizers and cognitive ability were examined in the absence of symbolic coding. The results indicated that cognitive ability had a positive effect on retention and generalizability and a negative effect on self—efficacy. In the second study, using a 2 (Symbolic Coding) X 2 (Pre- training Information) design, analyses also revealed a positive effect of ability on retention and generalizability. In addition, symbolic coding had an effect on retention through a mediating variable, learning. Implications for training and future research are discussed. ACKNOWLEDGEMENTS So many people have given me the support and assistance needed to make this thesis a reality. I hope I do them justice in the following short paragraphs. I would like to thank the faculty members at Michigan State for guiding my development, but in particular, the members of my thesis committee: Kevin Ford, Steve Kozlowski, and Neal Schmitt. Kevin Ford, my committee chair, provided me with the direction and support I needed throughout this process and guided me on how to ask the critical questions in designing research. Steve Kozlowski rendered invaluable assistance by constantly guiding me to think about theoretical implications and frameworks. Neal Schmitt was always available to furnish thoughtful insights on quantitative aspects and research manipulations. Without their guidance, I would still be writing a proposal, instead of an acknowledgements section. I would also like to thank my fellow graduate students at Michigan State. In particular, I would like to thank Rob Anderson for providing feedback and comraderie both academically and personally and for making graduate school a little easier to bear these last three years. Without making this an awards speech, I also want to thank Jose, iii Ron, Jen, Dennis, Whit, Stan, and El for providing insights, commentary, and support these last couple of years. In addition, I would also like to thank Andrea August, Michelle Hruska, and Dave Ziegler for their assistance during the data collection for this thesis. I also want to thank God for giving me the skills and abilities needed to make this thesis possible. I want to thank my family, especially my parents, for teaching me the value of striving to do my best, and providing me with the support I needed, not only through graduate school, but throughout my life. Last, but certainly not least, I want to thank my wife Kathy for not only providing me with love and support every step of the way, but for constantly reminding me what was really important in life. This accomplishment is as much hers as it is mine. iv TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES INTRODUCTION Advance Organizers . Conceptual History Empirical Work . Construction of Advance Organizers Symbolic Coding Cognitive Ability . Effects on the Processing of New Information Gathering and Storing Retrieval Summary Research Study Previous Research Retention . Generalizability. Self— -Efficacy Quality of Learning Points. METHOD . . Sample Design . Study I Study II . Independent Variables Pre- —Training Information/Advance Organizer Symbolic Coding Cognitive Ability Dependent Variables Learning . Reproduction Retention . Generalizability Self— -Efficacy Quality Procedure . Pre- -Training Information Conditions Videotaped Models . . . Symbolic Coding Conditions Tests for Learning and Reproduction vii Post-Training Measures Pilot Test RESULTS . Reliability of Measurement Study I . . Retention Generalizability Self— -Efficacy Study II . Retention Generalizability. Self— -Efficacy Quality DISCUSSION Summary and Implications of Major Findings Cognitive Ability Pre- -Training Information Symbolic Coding Study Limitations Pre- -Training Information Training Material . . Dependent Variable Measures Directions for Future Research APPENDIX A: Advance Organizer APPENDIX B: Background Information Paragraph APPENDIX C: Wonderlic Personnel Test — Cognitive Ability Scale APPENDIX D: Learning and Retention Scale APPENDIX E: Reproduction Scale APPENDIX F: Generalizability Scale APPENDIX G: Self-Efficacy Scale APPENDIX H: Quality of Learning Points Scale APPENDIX I: Positive Model Script APPENDIX J: Negative Model Script APPENDIX K: Learning Points APPENDIX L: Pilot Testing Results LIST OF REFERENCES vi 103 106 110 114 119 120 122 125 127 128 129 Table Table Table Table Table Table Table Table Table Table Table Table 10: ll: 12: LIST OF TABLES Interrater Correlation Matrix for Reproduction Scale . . . . . . . . . . . . . . . . . . . . . 61 Interrater Correlation Matrix for Generalizability . . . . . . . . . . . . . . . 63 Interrater Correlation Matrix for Quality . . . 64 Corrected Item-Total Correlations for Learning Scale . . . . . . . . . . . . . . . . . . . . . 66 Means and Standard Deviations of Study Variables for Study I . . . . . . . . . . . . . . . . . . 68 Intercorrelations Among Study Variables: Study I . . . . . . . . . . . . . . . . . . . . 69 Regression Analysis Results on Retention Measure: Study I . . . . . . . . . . . . . . . . . . . . 70 Regression Analysis Results on Generalizability: Study I . . . . . . . . . . . . . . . . . . . . 72 Regression Analysis Results on Self-Efficacy: Study I . . . . . . . . . . . . . . . . . . . . 73 Means and Standard Deviations for Study II . . 75 Adjusted Means for Dependent Variables in Study II . . . . . . . . . . . . . . . . . . . 77 Intercorrelations Among Study Variables: Study II . . . . . . . . . . . . . . . . . . . 78 vii Table Table Table Table Table l3: 14: 15: l6: l7: Regression Analysis Results on Retention: Study II . . . . . . . . . . . . . . . . . . . 79 Regression Analysis Results of Test of Mediation of Learning on Retention: Study II . . . . . . 81 Regression Analysis Results on Generalizability: Study II . . . . . . . . . . . . . . . . . . . 83 Regression Analysis Results on Self-Efficacy: Study II . . . . . . . . . . . . . . . . . . . 85 Regression Analysis Results on Quality: Study II . . . . . . . . . . . . . . . . . . . 87 viii Figure 1. Figure 2. Figure 3. LIST OF FIGURES Hypothesized Effects of Pre—Training Information and Symbolic Coding on Retention . 38 Hypothesized Effects of Pre-Training Information and Symbolic Coding on Generalizability . . . . . . . . . . . . . . . 44 Hypothesized Effects of Pre-Training Information and Symbolic Coding on Self-Efficacy . . . . . . . . . . . . . . . . . 48 ix I NTRODUC TI ON Billions of dollars are being spent in today’s workplace with the hopes of imparting new knowledge on the workforce (Michilak, 1981). However, much of the knowledge and skills learned in training are either not retained or are difficult to generalize from the training situation to the actual work. Such training is not only a waste of time for the trainees, but a financial loss for the organization sponsoring the training. Pre-training factors such as a trainee's readiness or a trainee's motivation in the training have a substantial influence on the effectiveness of various training approaches. Another pre-training factor is the trainee's previous knowledge base and the structure of that knowledge. Training programs often do not take into account the trainee’s knowledge background in the construction of various programs. One example is the training of assertiveness skills to employees. Training may provide the knowledge of how to be assertive, and even demonstrate the appropriate steps to take in a situation. Yet, the trainee may have learned to be assertive in a particular situation exemplified in training, and may be unable to transfer this newfound skill to a different situation that arises on their 2 own individual job. If the training had been structured around the background knowledge already in the trainee's cognitive framework, this new information might not only be encoded more accurately and retained longer, but a general structure could be provided that was not situation-specific. Taking an information processing view, this study examines the linkage between previous knowledge and new learning in a behavior modeling context. The process of acquiring new information involves effectively gathering, storing, and retrieving new material. The proficiency of this information processing can have an effect on the outcomes of training programs. This research study examines three issues that can affect the gathering, storing, and retrieval of new information which, in turn, can affect such training outcomes as the retention and generalizability of new training material, a trainee’s self-efficacy and the quality of generated learning points. First, researchers have suggested that the use of advance organizers, introductory materials constructed at a high level of generality and inclusiveness to serve an assimilative role for new learning material, could be beneficial in their application to training (Goldstein, 1991; Howell & Cooke, 1989). Not only should the trainee’s background knowledge be taken into account, but advance organizers provide a method by which this background knowledge can be used to increase the learning, retention, and generalizability of training material. What these mechanisms are and how they 3 can be applied to aid in the efficient processing of information, however, has never been fully understood in the context of training in work organizations. Second, a key component of behavior modeling training is the use of learning points to serve as a summary of the key behaviors required to perform the task (Decker & Nathan, 1985). The second factor in this study is the symbolic coding of these learning points through different methods. In particular, providing trainees with learning points and having trainees’ generate their own are two symbolic coding methods that are examined in this study. Third, cognitive ability has been shown to be a useful predictor of job performance (Dunnette, 1976; Hunter, 1986) and skill acquisition (Ackerman & Humphreys, 1990). Individuals with higher levels of cognitive ability are viewed as being more efficient processors of information in gathering, storing, and retrieving new information in a more organized fashion. Cognitive ability's effect on linking previous knowledge to training material is also explored in this study. In summary, all three variables have been suggested to aid in not only the gathering and storing of new information, but in the retrieval of this information as well. This study examines how they jointly affect the information processing of new material in the context of the training outcome measures of retention, generalizability, self-efficacy, and quality of learning points generated. Advance Organizers Conceptual histogy. Advance organizers were derived from the work of Ausubel (1963, 1968) in relation to new meaningful learning, or the learning of material that specifies an incorporation of new information into the individual's cognitive structure. Ausubel asserted that the individual's existing cognitive structure, both the content of the knowledge and its organizational properties, is the most important factor influencing meaningful learning and retention. Meaningful material is learned in relation to the individual's previous learned background of concepts, principles, and information. The more clear, stable, organized, and accurate the individual's background, the more likely it will be that new information will be learned and retained (Ausubel, 1968). In meaningful learning, it is impossible to think of new learning outside the realm of one's existing cognitive structure because this new information must somehow be incorporated into the individual's existing cognitive structure. Prior experience is conceptualized by Ausubel (1968) as a cumulatively acquired, hierarchically organized, established body of knowledge which can be related either positively or negatively to the new learning task. Ausubel defines this transfer situation as existing whenever an individual’s cognitive structure influences new cognitive functioning, irrespective of whether it is in regard to initial learning or problem solving (Ausubel, 1968). 5 Several factors influence the learning and retention of new meaningful information (Ausubel, 1968). The first factor is the availability in the cognitive structure of relevant anchoring ideas at a level of abstraction or inclusiveness appropriate to provide optimal reliability and anchorage for the to-be-learned material. The second factor is the extent to which the new information is discriminable from the established structures into which it is assimilated or that might be assimilated into the new information. Ausubel (1968) asserts that discriminable categorical information (information of a category that “stands out“ from other information) of more inclusive established meanings are more likely to be retained over the long-term. The third and final factor that affects the learning and retention of meaningful material is the stability and clarity of the anchoring ideas. If the anchoring ideas are ambiguous and unstable, they not only relate poorly to the new material, but they also cannot be easily discriminated from them. For example, an unstable anchoring idea will change in its relationship to the to-be-learned material depending on factors like the situation. The concept of anchoring ideas and the factors affecting the learning and retention of meaningful material led to the concept and application of advance organizers. Advance organizers are the principal strategy espoused by Ausubel (1968) to enhance the learning and retention of new meaningful material. These advance organizers are 6 introductory materials constructed at a high level of generality and inclusiveness whose relevance to the learning task is made explicit, to serve an assimilative role, rather than relying on the spontaneous availability or use of appropriate anchoring ideas in cognitive structure (Ausubel, 1968). These organizers are not to be confused with summaries and overviews which present information at the same level of abstraction, generality, and inclusiveness as the learning material itself by emphasizing the important points of the material and omitting the less important information. Advance organizers, on the other hand, serve the function of bridging the gap between what the individual already knows and what he/she needs to know before he/she can successfully learn the task at hand. The rationale for using advance organizers is based primarily on the importance of using relevant and appropriately established ideas already in an individual's cognitive structure to make new learning material potentially meaningful and give the material stable and clear anchorage (Ausubel, 1968). The use of advance organizers is also based on the advantages of using the more general and inclusive ideas or subsumers for the new information. In addition, advance organizers identify the relevant content in the individual's cognitive structure and their own relevance for the new learning material. Advance organizers have two functions (Ausubel, 1968): (1) to provide a structure for the learning and retention of 7 more detailed and differentiated material that follows in the learning material, and (2) to increase the discriminability between the learning material and similar or conflicting ideas already in the individual’s cognitive structure. The organizers not only give the learner a general structure of the more detailed information in advance of his/her actual confrontation with it, but also provide organizing elements that are inclusive of and take into account the particular content contained in the material. Ausubel (1968) makes note of two kinds of advance organizers. The first kind of advance organizer is an expository organizer that is used with material that is unfamiliar to the learner. These subsumers furnish ideational anchorage in terms that are already familiar to the learner. In learning unfamiliar material, this organizer might include whatever established and relevant knowledge presumably exists in the individual’s cognitive structure that would make the learning material more easily comprehended. A second kind of advance organizer is the comparative organizer that is used in the case of relatively familiar learning material. Its function is to integrate new ideas with similar concepts in cognitive structure, as well as to increase the discriminability between new and existing ideas which are different but can be easily confused. This organizer might point out ways in which the two concepts are sindlar and different. The use of either 8 of these organizers makes the relevance of the anchoring ideas to the learning material more explicit and is itself more related to the differentiated content of the material to be learned. Empirical work. Substantial amounts of research on the effectiveness of advance organizers can be found in the educational literature. While this research contains conflicting results regarding the effectiveness of advance organizers, some basic tenants regarding the effective use of advance organizers can be stated. This section highlights some of those common themes in relation to the use of advance organizers. The research on advance organizers begins with Ausubel’s series of studies designed to examine the effects of advance organizers on learning from text (Ausubel, 1960; Ausubel & Fitzgerald, 1961, 1962; Ausubel & Youssef, 1963). In a typical study, (i.e., Ausubel & Youssef, 1963), subjects would read either a comparative advance organizer displaying the differences between Buddhism and Christianity or a historical introduction prior to reading a passage on Buddhismi Retention for the passage was higher for the advance organizer group based on the assumption that the advance organizer helped the subjects learn Buddhism using existing concepts in their own cognitive structures. Recent work has tested the parameters of Ausubel's subsumption theory, providing a slight modification on his work. This work has examined the validity of fundamentally 9 different views of how an advance organizer influences learning (Derry, 1984). On one hand, the assimilation encoding hypothesis proposes that an advance organizer will facilitate the process by which material is subsumed by more inclusive anchoring ideas. In this subsumption, the material to be learned and the anchoring knowledge are combined so that the details of the discourse are obscured and the higher order ideas that are related to the schema are enhanced (Mayer 1975a). On the other hand, Ausubel proposed the schema + correction hypothesis which argues that advance organizers do not obliterate the relevant factual detail in the “to be learned“ material. Instead, advance organizers increase the discriminability between the text information and the anchoring ideas. Derry (1984) tested the differences between Mayer’s assimilation encoding view, Ausubel's schema + correction view, and a third view which combines the two views mentioned above entitled assimilation + correction. The assimilation encoding view describes the integrating function of the organizer on incoming information and the schema + correction view highlights the role played by the organizer in enhancing the discriminability of new material from the anchoring ideas of the subsumer. The assimilation + correction view asserts that both occur simultaneously as part of the same learning mechanism (Derry, 1984). This system suggests the operation of a dual-encoding system whereby schema-consistent ideas are assimilated into prior 10 active knowledge, and novel and inconsistent details are added to memory as discrete schema corrections. Schema- consistent information is thought to lose detailed specificity at encoding and will be difficult to be retrieved at a later date, whereas modifying details should be enhanced (Derry, 1984). The results of Derry’s (1984) research favored the assimilation + correction view. The prevalent effect was assimilatory loss of detail associated with information consistent with the schema (Derry, 1984). Two meta-analyses have examined the effects of advance organizers on the learning and retention of information (Luiten, Ames, & Ackerson, 1980; Stone, 1983). These meta- analyses have found small, but facilitative effects for advance organizers in measuring learning and retention. In his review of the advance organizer research, Mayer (1979b) developed four important parameters for the use of advance organizers in regards to the structure of the learning material, the characteristics of the subjects, and the outcomes of using advance organizers. These parameters can help to explain the small, facilitative effects found in the research. First, advance organizers have a stronger effect for poorly organized or structured material than for well organized material. This finding has been fairly well documented in the advance organizer research (Daniels & Whitman, 1981; Mayer, 1978; Schumacher, Liebert, & Pass, 1975). If the material is not well organized, the advance organizer provides the learner with a structure to encode 11 the new material. Second, advance organizers should have a stronger positive effect for learners lacking prerequisite knowledge. While supported by Ausubel’s work (Ausubel & Fitzgerald, 1961, 1962; Ausubel & Youssef, 1963), subsumption theory predicts that advance organizers should have a stronger effect for learners who lack the prerequisite knowledge. Those who already possess the prerequisite knowledge have their own structure in regards to the new material. Third, advance organizers should have an especially strong effect on measures of transfer rather than retention. Advance organizers provide the individual with a general structure that is applicable to more than one situation. For example, if the goal of training is to instruct individuals on a three—step process to turn on a machine, advance organizers would not be necessary for retention. However, if you were instructing individuals on a skill that would need to be applied to a variety of different situations, an advance organizer could aid in the generalizability by providing a general structure for the learner. While most of the research on advance organizers has been in the area of learning and retention, issues of far transfer and generalizability need to be addressed. Research by Mayer (Mayer, 1975a, 1975b, 1976) found that subjects in an advance organizer (A0) condition performed better on far transfer items and control subjects performed better on near transfer items with far transfer items being 12 those that require the adaptation of knowledge to new situation and near transfer items being those that require rote memorization of information. In another study, Mayer and Bromage (1980) found that AC subjects recalled different types of information than control subjects and subjects who received the organizer after the learning material (P0). The A0 subjects tended to recall more idea units concerning conceptual aspects, more intrusions concerning the model, and more novel interpretations concerning the material which would be more suited for situations requiring the generalizability of information. The control group and PO group recalled more technical and specific idea units which would be more suited for situations that require rote memorization. Fourth, advance organizers should have a stronger effect for learners lacking prerequisite cognitive abilities (Mayer, 1979b). High ability subjects are typically those experienced learners who more readily use their own existing knowledge as an assimilative set during learning, even without the assistance of an advance organizer. Low ability learners do not necessarily use their existing knowledge as readily as higher ability learners; therefore, advance organizers should provide a larger benefit for those with lower abilities. Of these parameters, a major point of conflict concerns the issue about the differences in ability levels. In regards to the four parameters mentioned above, the results 13 of the meta-analyses (Luiten, Ames, &Ackerson, 1980; Stone, 1983) tend to conflict with the contention that low-ability subjects benefit the most from the use of advance organizers. Both meta—analyses not only refute this statement by saying that both high-ability and low-ability subjects benefit from advance organizers, but in one meta- analysis (Luiten, Ames, & Ackerson, 1980) the authors argue that high-ability subjects might benefit more from the use of advance organizers. While this notion conflicts with Ausubel’s subsumption theory, a possible limitation of the meta-analyses is that not all of the studies measured ability in the same way. In addition, not all advance organizers are well constructed in this research which could also lead to conflicting results. This fact will be expanded upon in the next section. In studies that are well constructed (i.e., Mayer, 1975a), lower ability subjects tended to benefit more from the use of advance organizers than high ability subjects. Construction of advance organizers. A common criticism of advance organizers is the operational construction of an advance organizer in relationship to subsumption theory proposed by Ausubel (1968). Many critics argue that there is no clear, concise, or standard way to construct advance organizers, and most researchers tend to construct the organizers based on the notions and writings of Ausubel (Barnes & Clawson, 1975; Clark & Bean, 1982; McEneany, 1990). This criticism questions not only the nature of the 14 studies conducted, but the results obtained from the meta- analyses listed above (Luiten, Ames, & Ackerson, 1980; Stone, 1983), as well as other reviews that conclude advance organizers have a small facilitative effect on learning and retention. If they are poorly constructed in a study (i.e., uses titles and main points instead of subsumers), it is no surprise that they have small facilitative effects, if any at all, because they violate Ausubel's principles for constructing an advance organizer (Mayer, 1979b). Solutions to the problem of poorly constructed advance organizers can be found in a variety of ways. In his theory, Ausubel (1968) described advance organizers as being presented at a higher level of abstraction, generality, and inclusiveness. Mayer (1979a) defined an advance organizer as generally having the following characteristics: (1) a short set of verbal or visual information, (2) presented prior to learning a larger body of to-be-learned information, (3) containing no specific content from the to-be-learned material, (4) providing a means of generating the logical relationships among the elements in the to-be-learned material, and (5) influencing the learner's encoding process. In practice, these organizers have been in the form of outlines, questions, graphic displays, and abstract passages. While fitting Ausubel's description of relevant and inclusive introductory materials, no clear consensus has been reached on how to construct advance organizers. Often, advance 15 organizers that have a positive effect do not necessarily follow Ausubel's prescription for abstractness (Corkill, Glover, & Bruning, 1988). Effective organizers reported in the literature have a tendency to be concrete instead of abstract (Mayer, 1979b; Stone, 1983). One can explain the superiority of concrete organizers over abstract organizers because the former are more familiar to the learner (Paivio, 1983), more easily remembered than abstract materials (Marshark, 1985), and more easily visualized than abstract materials (Paivio, 1983). However, some investigators have argued that this is not a problem for Ausubel’s subsumption theory because a concrete organizer would allow the formation of memorable schemata or call up prior knowledge for assimilating new information better than an abstract organizer (Corkill et a1, 1988). In addition, some evidence has been found that organizers which are concrete, oral, and visual are more effective than written ones (Luiten et al, 1980; Stone, 1983). However, a confound in this research is that most written organizers tend to be abstract, while most nonwritten organizers tended to be more concrete. In working with the assumption that concrete organizers are more effective than abstract organizers, analogical techniques could be highly useful. The literature concerning the use of analogical reasoning, particularly in problem solving, has not been widely referenced in relation to the use of advance organizers. The most likely reason 16 for this omission is that a majority of advance organizer work deals with the learning of prose material. Analogical problem solving takes into account the application of knowledge and not just the rote learning and retention that is typically examined in advance organizer research. In problem solving research, people deal with new events by retrieving information about holistically similar experiences (episode-based processing) or about abstracted characteristics of those experiences (rule-based processing). In episode-based processing, individuals access previous problems that are similar analogically or in other ways. The problem is similar to one they have previously experienced and they map the solution of the previous problem onto the new one (Holyoak, 1984). In contrast, rule—based processing occurs when peOple form abstract schemata that characterize sets of problems sharing similar structures (Gick & Holyoak, 1983, 1987). The presentation of an analogical model of the domain during training should encourage people to identify and abstract rules and concepts in the domain. These analogical models encourage people to integrate new information with a pre- existing meaningful knowledge base (Mayer, 1979b). In terms of the organization of material, subjects tend to use episode-based processing when material is presented randomly and rule-based processing when material is presented in an organized fashion (Homa, 1984). The complexity of the material also has an effect on 17 the type of processing because the more complex the task/material, the more people must make multiple decisions which can be open-ended in nature. When material is complex, subjects tend to abstract rules and structures. Fried and Holyoak (1984) found that when subjects were exposed to highly variable examples of artificial categories, they were more likely to demonstrate transfer of the material when new exemplars were presented. In terms of transfer appropriate processing (episode vs. rule-based). performance on a memory task should be best when the processing required at retrieval is similar to that evoked at encoding (Brooks, 1987). If two events are processed in the same way, the processing of the second can serve as a retrieval cue for the first. In a series of studies, Mayer (1975a, 1976) provided empirical support for this proposition. Mayer demonstrated that providing people with analogical models for the material they are learning has systematic effects on later performance. Such models generally improve individuals’ understanding of underlying concepts and performance on complex, far-transfer tasks. In contrast, they have no effect on simple, near-transfer tasks (Mayer 1975a, 1976; Mayer & Bromage, 1980). In general, when tasks are complex, model—provided training is beneficial and when tasks are simple, no-model provided training is beneficial. In a recent problem-solving study, Caplan and Schooler (1990) found empirical support for the use of models and organized 18 instructions for complex tasks and they found support for the use of no-models for simple tasks. Also, in simple tasks the organization of instructions was not essential. In a series of experiments, Dinnel and Glover (1985) proposed that how well the advance organizer is encoded should influence its effectiveness in facilitating learning from.prose. An advance organizer encoded in a way that requires attention to its semantic base would be better recalled than an organizer encoded more superficially. The authors assert that the conflicting results in the literature are partially attributed to the fact that organizers are not encoded very well (Dinnel & Glover, 1985). In their experiments, it was found that subjects who paraphrased the advance organizer prior to receiving the learning material were able to recall more information. The authors do note that this paraphrasing does require more time in the training/educational setting, however, the positive effects were well worth it (Dinnel & Glover, 1985). In addition, Kloster and Winne (1989) found that simply presenting a genuine advance organizer does not guarantee that individuals will use it effectively. However, when individuals did use an advance organizer effectively, their achievement increased, especially when the organizer was of a concept or analogy nature. Advance organizers typically come before the material to—be-learned to set up a mental schema to encode this new material. In a series of experiments, Mayer and Bromage 19 (1980) presented either an organizer before or after reading a text concerning a new computer programming language. The before group scored higher on recall of conceptual idea units, produced more appropriate intrusions, and made more novel inferences. The after group scored higher on technical idea units, but produced more inappropriate intrusions, connectives, and vague summaries (Mayer & Bromage, 1980). Their results support the notion that the locus of the effects was at the time of encoding rather than at the time of retrieval. Overall, advance organizers have generally been used with tasks involved with the learning of prose. Their effect, especially in a behavior modeling training environment, on skill acquisition has not been thoroughly explored. This study examines skill acquisition in a behavior modeling training context. Advance organizers should be more effective with material that is poorly structured and with measures of transfer over retention (Mayer, 1979b). Complex skills, especially those of a social nature, need to be transferred to various situations more than a simple task that is performed the same way each time. With the learning of a complex skill, advance organizers should serve an assimilative role for the learning of new material by providing anchoring ideas at a high level of inclusiveness. The advance organizer should provide a structure to assimilate the new learning material with the individual's prior knowledge and should enhance not 20 only the retention of complex skills, but their generalizability to different situations as well. In addition, this study explores their effectiveness across various levels of ability. Symbolic Coding As a training technique, behavior modeling (Goldstein & Sorcher, 1974) has proven to be an effective device in the acquisition of skills, particularly those of a manual or social nature (Decker & Nathan, 1985; Latham & Saari, 1979). Based to a large extent on social learning theory (Bandura, 1977), behavior modeling enables the learner to observe others in a model performing the to-be-learned task and to learn the proper methods and techniques required for the skill/task. The purpose of this review is to examine how to best train individuals to symbolically code information received in behavior modeling training. Behavior modeling typically involves five components: (1) a modeling component where trainees observe the behavior, (2) a retention component where trainees try to retain the information, (3) a behavioral rehearsal component where trainees practice the modeled performance, (4) a feedback component where comments are provided to the trainees, and (5) a transfer of training component where the trainees attempt to generalize the learned material (Decker & Nathan, 1985). The training involved in this study deals primarily with the modeling and retention components of behavior modeling training. How best to get trainees to 21 code symbolically, whether providing them with rule codes or having them generate their own, is examined in this study: With behavior modeling training, subjects are first provided with a model that displays a set of key behaviors. The modeling component contains learning points which are written descriptions of key behaviors that must be used to complete a task (Decker & Nathan, 1985). These learning points can be behavior descriptions which are simple specific descriptions of the behavior performed (“Say hello to the applicant"), summary labels which are key words used to define or cue certain behaviors or classes of behaviors ("greet applicant“), or rule-oriented codes which specify underlying sets of behaviors which can be used to complete the task but do not necessarily include a description of the actual behavior (“greet applicant warmly and have the applicant sit down so you can start the interview“). With simple manual skills, the behavior descriptive learning points are preferable. With social skills or tasks that require complex cognitive abilities, rule-oriented learning points are more effective as there is more than one way to complete the task. (Decker & Nathan, 1985). In other research, Baldwin (1992) examined the effects of alternative modeling strategies on the outcomes of interpersonal—skills training. He found that the presentation of multiple scenarios had no effects on trainee reactions, learning, retention, and behavioral measures of reproduction and generalization. On the other hand, models 22 that displayed positive and negative displays had a significant positive effect on trainee generalization and a significant negative effect on reproduction. Mann and Decker (1984) examined the effect of key behavior distinctiveness on generalization and recall. Making key behaviors with low or moderate natural distinctiveness more distinctive enhanced attention to and retention of modeled events, but did not have the same effect for those key behaviors which were already naturally distinctive. In addition, they found that observing learning points and a form of a model (combined or with learning points interspersed in the model) significantly facilitated generalization for the dimensions shown to have moderate to low natural distinctiveness. The dimension with the highest distinctiveness was less affected by the various combinations of learning points and models. However, generalization scores still were significantly greater for subjects who saw learning points and a model than for those who saw learning points only. The authors recommend the use of an interspersed condition, whereby the learners view the model, then view the learning points, and then view the model with the learning points displayed just before the key behaviors appear in the model. This condition is preferred because it is anticipated that both contrast and meaningfulness would increase as learning points and behavior became more closely linked (Mann & Decker,-1984). In viewing the model, the question arises as to “How 23 would subjects best encode the learning material?" Various retentional processes help the trainee remember and retain what was seen in the modeling display. These retentional processes include the building of better models as mentioned above, the use of symbolic coding and rehearsal, as well as the generation of learning points by the trainees themselves. Research by Decker (1980, 1982) highlights the importance of a formalized retention process over any retention processes performed by the trainees spontaneously. The author defines symbolic coding as the process by which individuals organize and reduce the diverse elements of a modeled performance into a pattern of verbal symbols that can be easily stored, retained intact over time, quickly retrieved, and used to guide performance. Symbolic rehearsal is the process in which individuals visualize or imagine themselves performing behaviors that previously were seen performed by another individual (Bandura, 1977). Training that includes both symbolic coding and rehearsal significantly facilitated the generalization of observational learning to a novel context (Decker, 1982). In other research, the use of trainee-generated codes over trainer—produced codes has been examined. Decker (1982) found that trainee-generated rule codes in assertiveness training enhanced generalization and displayed the least amount of reproduction decay. Along similar lines, Hogan, Hakel, and Decker (1986) addressed the effect of trainee- generated codes on generalization. The authors hypothesized 24 that trainee-generated codes would yield greater generalization assuming that code generation requires greater initial information processing and results in more meaningful codes that are better integrated into the unique cognitive framework of each individual trainee. In their study, self—generation of rule codes led to significantly better performance on generalization tests given one week after training. Content analysis of the trainee-generated codes revealed that they were conceptually similar to but of lower quality than the trainer-provided codes. Even though the codes were of lower quality, they were retained longer than the higher quality codes provided by the trainer. In addition, there was no difference between conditions in participant reactions to the training (Hogan et al, 1986). (The authors list some qualifications to their conclusions concerning trainee-generated codes: (1) trainee competence and bias should be considered in determining the optimal strategies for rule coding procedures, and (2) a high quality modeling display based on thorough job analysis is a prerequisite for the use of trainee-generated codes (Hogan et al, 1986). This study further explores the effect of symbolic coding on the retention and generalizability of training material and the effect of symbolic coding on trainee self- efficacy. How best to induce the symbolic coding of training material, whether through trainer-provided codes or trainee-generated codes is examined. While not a complete 25 behavior modeling training session (which includes behavioral rehearsal and feedback), using a videotaped model and learning points enables a trainee to more effectively encode skill material than instructing trainees using such formats as lectures. By providing the model and learning points, the trainees could be better prepared to practice, the learning material. Coggitive Ability Individual differences in cognitive ability have long been associated with differences in job performance (Dunnette, 1976; Hunter, 1986) as well as skill acquisition (Ackerman & Humphreys, 1990). Ackerman (1987, 1989) has expanded the work of Norman and Bobrow (1975) to describe individual differences in cognitive ability as differences in the amount of cognitive or attentional resources available to that individual. A person with higher cognitive ability should have more attentional resources available to engage in tasks. This is particularly beneficial in complex tasks that require more attentional resources (resource-dependent) in their acquisition. In learning a skill, cognitive ability has its greatest effects early in the skill acquisition when the task is novel. However, this effect is not as strong in later performance when the individual has practiced the task tsufficiently (Ackerman & Humphreys, 1990). This effect is oum mucflom engaged: and :OwquLOMEH pasoumxopm u Hm Lunaccmuo monp>p< u 04 75 «man: 35 Rd Ba Ba .3225 b¢.o om.b mm.H ma.b mN.H mm.m oo.H ma.b hupowummimawm mm.o mm.m Hm.o mm.m Nb.o mb.m vb.o or.m >uwawnmnwamumcmo 0N.N mH.MH NM.H mv.mH mb.m hm.NH Hm.N mv.va coflucmumm mm.m mm.HH mm.H mN.mH mv.N om.HH vm.H mm.va magnuqu mH.fl mo.mm mb.v mm.mm wv.¢ bo.vN Ho.m m¢.mm huflaflnd .mOU mm.o om.m fib.o NH.m «v.0 mo.m 05.0 mm.m cofiuozpouawm om new: am and: am new: am new: mm»: mm": on": Hm": 0mg and 0&4 and 6 HQ w Hm w Ofl w Cd HH aosum no“ unawuuw>mo Unaccoum one mcooz ca manna 76 in Table 11 and the correlation matrix for the variables is listed in Table 12. Retention. H5 stated that cognitive ability would have a positive effect on the retention of training information. This hypothesis was confirmed when entered into the regression equation first, producing an thof .07, p<.01 (see Table 13). Next, learning was entered into the regression equation producing a significant change in chof .46, p<.01. IL stated that individuals who received an advance organizer would retain more training information than individuals who received background information and H3 stated that individuals who generated their own learning points would retain more information than individuals who received learning points. Both were entered into the regression at the same time and produced a non-significant change in R?., disconfirming both hypotheses. Next, all two-way interactions, including exploratory analyses, among the four variables were entered into the equation. H4 predicted an interaction between the conditions of pre— training information and symbolic coding. Results found that the interaction between symbolic coding and pre- training information was not significant, thereby disconfirming the hypothesis. Exploratory analyses for the other two-way interactions also produced non-significant results. Finally, all three-way interactions were included in the final step. No significant interaction was discovered. The total variance accounted for by all the 77 Table 11 Adjusted Means for Dependent Variables in Study II AO & AO & BI & BI & LPP LPG LPP LPG n=31 n=30 n=28 n=33 Reproduction 2.88 3.18 3.00 3.39 Retention 13.61 13.48 14.26 13.99 Generalizability 3.67 3.83 3.80 3.93 Self-Efficacy 7.09 7.06 7.08 7.32 Quality 2.67 2.56 n=122 A0 = Advance Organizer BI = Background Information LPP = Learning Points Provided LPG = Learning Points Generated pobmuocoo nonwod mcficuooqufl .oopw>onm moceom unacuooauo “mcwooo oflaobs>m Homecomuo ooco>o¢ua .coflBMEAOMEH pesoumxommuo “.0ucH mcficfiouuumum 78 Ho.va u o mo.ve u n NNH u c oo.a mo.u Ho. we. um¢.u umm.n mo.u Am. mcflpoo oeeonssm oo.H we. no.1 oH.: ma.u mo.u mo. Ab. EOAunEHOucH newcwpuuuwum oo.H Ho. vm. new. nun. ma. Am. >ufiam=o oo.a vo. mo.n mo. ma.u Am. suaveeem-eflmm oo.H ma. mo. Ame. Aw. zuwflfinoufiaoumcmo oo.H cab. aha. .m. cofiucmuom oo.H ma. Am. newcuooq oo.a .Hv huwafina m>fluacmoo 2: . E a: E E E E 3 HH >ozum “moanofinp> xenon moose occauoamuuooumucH NH manna 79 Table 13 Regression Analysis Results on Retention: Study II VARIABLE Beta R R2 R2 Change Step 1: Cognitive Ability (C) —0.88 .27 .07 .07° Step 2: Learning (L) -0.37 .73 .53 .46c Step 3: Pre-training Info (P) -1.37 Symbolic Coding (S) -3.71 .74 .54 .01 Step 4: C X L 1.30 C X P 0.47 C X S 3.91 L X P 1.12 L X S 2.60 P X S -0.12 .76 .58 .04 Step 5: C X L X P -0.44 C X L X S -2.94 C X P X S 0.18 L X P X S 0.12 .77 .60 .02 n=122 a = p<.10 b = p<.05 c = p<.01 Note: Final Standardized Beta weights were used 80 variables for retention was .60. Exploratory hypotheses once again examined the large amount of variance attributed to learning as being a possible mediator for advance organizers. The correlation between pre-training information and retention was once again non-significant, thereby, disconfirming the idea of mediation. In further examination of the correlation matrix (see Table 12), the correlation between symbolic coding and retention was noticeably large (-.42, p<.01) suggesting that when learning points were provided, individuals retained larger amounts of training material. This was tested using learning as a mediator variable to see if its effects would hold true after accounting for the effects of symbolic coding on retention (James & Brett, 1984). To test for mediator effects, a separate regression analysis was conducted (see Table 14). Cognitive ability was covaried out of retention prior to conducting the test for mediation. In the first equation, symbolic coding was entered before learning. Symbolic coding produced a significant change in I? of .18, pg.01, when added to the equation after cognitive ability, and learning produced a significant change in R213f .28, p<.01 after cognitive ability and symbolic coding. In the second equation, learning was entered before symbolic coding. Learning produced a significant change in R?