SIMULATION PERFORMANCE REVISITED: THE FIT BETWEEN INSTRUCTOR STYLE AND LEARNING STYLE Developments in Business Simulation & Experiential Exercises, Volume 16, 1989 89 Simulation Performance Revisited: The Fit Between Instructor Style and Learning Style Peggy A. Golden, University of Louisville Jerald R. Smith, University of Louisville ABSTRACT The issue of simulation performance has been explored from many angles. This paper takes a fresh approach to the problem by looking at the individual learning style as it fits with the instructor style and the pedagogy. INTRODUCTION Simulation games continue to he incorporated into many business classes at an increasing rate. Although the effectiveness of experiential pedagogies is difficult to measure, proponents submit that learning is more operative when the students are involved in a relevant activity and the environment is responsive to the immediate needs of students [3]. Yet, most professors who use simulation games are aware that some students in each class do not have a productive learning experience in the simulation classroom and consequently do not perform well in the class. Kolb [7] suggests that an incongruence in the learning style of the individual may be a factor in the reduced effectiveness of simulations. He hypothesizes that s mismatch between learning styles and discipline demands have an impact on social adaptation of a student to the university. Moreover, he gives evidence that fir between discipline selection and learning style affect the perception between light and heavy work- loads [7, p. 178]. This paper presents a pilot study that explores the thesis that the fit between instructor style and learning style may explain individual performance in the simulation classroom. BACKGROUND Simulation and Performance Simulation performance is generally thought to he high if the team performs well as determined by several measures of corporate performance. Positive performance has been associated with group cohesiveness [5, 10], team size [11], group member aptitude and grade homogeneity [11], use of effective decision support systems 12], and a host of other variables. In a comprehensive review in the literature, Gosenpud [4] concludes that performance may be a yet unmeasured inter- action among a series of independent variables. In all cases, performance in considered to be a function of the team rather than an attribute of the individual. Thus, none of these studies address the fact that individual performance is the critical classroom issue and that the instructor is still forced to rank the individual. Within teams, individual members may be performing at varying levels resulting in nonconclusive results about the overall team performance. This issue is articulated by students who express dissatisfaction with the simulation/experiential class due to its lack of structure, focus on group work, or absence of “traditional” ambience. Individual Learning Style and Instructor Style Learning style is a complex construct that appears to consist of many dimensions including cognitive and affective processes, a longitudinal development of skills (Piaget) , sod the conflict and tension created by the group (Lewin). Although there is a tendency to measure learning in terms of outcomes, it may better be treated as a process with stages that have distinctive features. Kolb [7] tires this approach in the Learning Style Inventory (LSl) and suggests that individuals pass through discernable stages with attributes that may be addressed by different types of classroom content. The stages are identified along two dimensions: reflective observation vs. active experimentation, and concrete experience vs. abstract conceptualization. Each stage is associated with a “best” learning approach (LSI, P. 5). Four categories of ‘earning style are thus derived: diverger, assimilator, converger, and accommodator. Although the LSI has been subject to some criticism, the 12- item 1981 version has been validated over a wide range of professions, age groups and academic disciplines [7]. In addition, it has been used in studies associating it with other variables of interest to the authors including a study matching Bostrom’s Trainer Style Inventory [2] to the LSI categories [8]. Bostrom’s Trainer Style Inventory is a 60-item instrument that categorizes the trainer according to locus of control (internal vs. external) and cognition (sensory perception vs. analytical verbal). Scores on each dimension permit defining the trainer as having a behaviorist orientation (high sense perception/external locus of control), structuralist orientation (high analytical verbal/external locus of control), functionalist orientation (high analytical verbal/internal Locus of control), or humanist (high sense perception internal locus of control). Table 1 below highlights the characteristics of the Bostrom categories showing the match or fit with the LSI. Thus, one would expect to see better learning if the instructor style was that of a behaviorist and the learning style was that of an assimilator. Conversely, one would expect to see poorer learning between a behaviorist instructor and a divergent learner. Since neither categorization scheme implies a continuum, there is no one bettor or poorer fit; rather the fit relationship is dichotomous: fit or no fit. Developments in Business Simulation & Experiential Exercises, Volume 16, 1989 90 Instructor-Learner Fit and performance In the discussion above, the contingency notion of fit between the instructor and learner is proposed. It seems reasonable no assume that the performance of the individual will be higher if there is a “fit” situation than if there is a “misfit.” The simulation approach to classroom teaching appears to be most consistent with the instructor style of the functionalist. The basic assumptions of the functionalist are that performance on the lob is a true test sod that the instructor role is one of coach. It may also be consistent with the behaviorist. The basic assumptions of the behavioralist are that learning is a process that has desired end results which can he achieved through reinforcement; therefore the behaviorist creates so environment of complete security for learners. Both of these instructor styles are consistent with experiential classroom activities such as simulation games. In the behaviorist classroom, the emphasis may be on practicing while in the functionalist classroom, the emphasis is on problem-solving. Conversely, the structuralist depends on presentation techniques including carefully prepared lectures, and the humanist focuses on freedom and individuality 121. Similarly, one would expect the Accommodator who learns best from hands-on activities sod the Assimilator who can apply abstract concepts to a wide- range of information to outperform the Converger and the Diverger in simulation-type activities Figure 1 below describes these contingency relationships. METHOD A pilot study was conducted at a large, Midwestern University using 102 undergraduate and graduate Business School students in Business Policy and Business Environment classes as subjects. All students were involved in a class situation in which a simulation game was the primary determinant of classroom performance. The simulation for all classes in the study was CORPORATION: A Business Strategy Simulation [9]. The simulation portion of the class included regular derisions for the simulation, periodic control analyses, peer review, and an annual report to stockholders. All students were administered the Learning Style Inventory during the first class session. The instructors were administered the Bostrom Training Style Inventory. Performance was determined by the syllabus-weights on individual and group simulation activities. Overall firm performance was judged by: 1. Performance Points calculated by the simulation. 2. Overall cash management. 3. Cumulative return on sales, 4. Stock price. 5. A subjective ranking of the team by the instructor based on involvement and ability to maintain good group relationships. Individual grades were assigned based on the quality of individual analyses (graded and returned each decision period) and peer review scores from teammates. Each individual was thus giver a simulation score of 1-6 where 6 corresponds to an A and 1 corresponds to a C (this is the standard method of simulation grading at this university). RESULTS In order to test the hypothesis that fit between Bostrom’s Training Style and Kolb’s learning Style resulted in performance, frequency tables were formed using SAS Version 5.5. Performance categories of law, medium and high were formed by collapsing individual performance scores. Compatibility was determined in the following manner’ accommodators (Kolb) in the functionalist classroom (Bostrom) and assimilators (Kolb) in the behaviorist (Bostrom) classroom were classified as compatible. All other combinations of LSI scores and Bostrom Training Style scores were classified as incompatible. Table 2 below summarizes the frequencies of classification. Although the Chi- square statistic was significant (X2 a 7.589 p .05), the frequency distribution indicated that incompatibility was associated with high performance. On the basis of this analysis, the fit hypothesis would need to he rejected. A more detailed analysis was performed to see if the source of the negative results could be pinpointed. Table 3 below summarizes the frequency tabulations of LSI and performance controlling for instructor style. In this analysis, it was deer that the Converger learning style controlled the fit relationship since the Converger produced high performance (56.257 for the Behaviorist and 33.33 for the Functionalist) even though the literature did nor suggest that this style would fir either the simulation pedagogy or the instructor styles hems used. In referring back to the learning Style Inventory, it appeared that the Converger would produce a high score on the dimension of Active Experimentation-Reflective Observation. Pearson r’s were computed for the four anchors of the LSI with the raw performance score. As Table 4 indicates, Active Experimentation has a r=.274 (p - .01) with Performance and Reflective Observation has an r = -.302 (p - .01). Correlations for the other axis (Concrete Experience- Abstract Concepts) were not significant. This can be interpreted as meaning that the use of Active Experimentation as a Learning Style has greater predictive value than the entire learning Style category. Developments in Business Simulation & Experiential Exercises, Volume 16, 1989 91 The hypothesis that Learning Style predicted simulation performance was tested with a frequency table. Table 5 below shows the anomalous fact that although approximately 547 of the high performers were in the predicted categories of Assimilators and Accommodators, these two categories were responsible for 71% of the low performers. It is interesting to note that in all analyses (Tables 2,4 and 5) the Diverger did not score high in performance. This may be a result of the Diverger’s tendency toward Reflective Observation. Policy course. An additional analysis of learning style and performance as moderated by discipline may produce more robust results. REFERENCES Anderson, P. and Lawton, L. “Integrating Personal Computers into a Course as a Decision Support Tool” Developments in Business Simulation and Experiential Learning Vol. 13 212-214 Brostrom, R. "Training Style Inventory” in Jones. J.E. sad Pfeiffer, J.W. ed The 1979 Annual Handbook for Group Facilitators San Diego: University Associates 1979 Bracker,J., Brenenstuhl, O. and Gwinner, R.F. “Experiential Learning in the International Environment” Developments in Simulation Games and Experiential Learning Vol. 15 93-96 Gosenpud, Jerry “Research on Predicting Performance in the Simulation” Developments in Simulation Games and Experiential Exercises Vol. 14 1987 75-79 Hsu, T. “A Further Test of the Group Formation and Its Impacts in a Simulated Business Environment" Developments in Business Simulation and Experiential Learning Vol. 11 1984 6-9 "Training sod Learning Styles” Infoline April, 1988 1-16 Kolb, D. Experiential Learning Englewood Cliffs, NJ: Prentice-Pall, 1984 Moore, A.B. ,1984 Smith, J, P. and Golden, P.A. Corporation: A Business Strategy Game Englewood Cliffs, NJ: Prentice-Hall 1989 Wolfe, J. and Chacko, T.I. “Team Size Effects on Business Game Performance and Decision-Making Behaviors” Decision Sciences Vol. 14 121-133 Wolfe, J. and Box, T.M. “Team Cohesion Effects of Business Game Performance” Developments In Simulation Games and Experiential Exercises Vol. 14 1987 250-254 DISCUSSION This study did not produce the conclusive results that would permit one to associate simulation performance with either individual learning style characteristic or some contingency relationship between the instructors style and the learning style. However, the role of Active Experimentation as a factor In learning style seems to have some predictive value. This suggests chat one would find the greatest benefit in simulation and other experiential exercise at that point in the educational development at which experimentation is the active mode. Kolb’s studies indicate that this point is in the later stages of the learning cycle and are associated with certain careers that are action oriented. Therefore, simulation performance might improve in those universities in which there is a two- semester Business Table of Contents Volume 16, 1989 Quality Control Circles (QC™s): Towards a Computerized Simulation The Canadian Hospital Executive Simulation System (CHESS) The Impact of Using Group Performance Evaluation as an Experiential Exercise The Impact of Leader and Team Member Characteristics Upon Simulation Performance: A Start-Up Study Planning for Career Success: Is Where you are Going Where you Really Want to Be? The Production Frontier: Modeling Production in the Computerized business Simulation A Study of the Need for Valid Business Game Algorithms Modeling the Human Component of Business Simulations A Stimulating Simulation in International Business Business Ethics, Experiential Exercises and Simulation Games Collective Bargaining Simulation: Adding Reality Through Point Scoring The Use of Experiential Teaching Techniques: Creativity vs. Conformity Visualization and Guided Imagery in the Organization Behavior Class: An Experiential Exploratory Approach Arranging an Agenda: An Activity on Running Better Meetings Harried Harry: An Experiential Capstone for Students of Organizational Behavior Coping with Stress: An Experiential Exercise Fairness in the Classroom: An Empirical Extension of the Notion of Organizational Justice A Study of the Relationship Between Student Final Exam Performance and Simulation Game Participation Competency Based Development: A Management Development Exercise Simulation Performance Revisited: The Fit Between Instructor Style and Learning Style An Evaluation and Application of an Instrument for Measuring Pedagogical Effectiveness A Knowledge Based System to Support Reasoning by Analogy for Business Simulation Gaming using Forecasting Accuracy as a Measure of Success in Business Simulations The Development of Algorithmic Functional Business Games Strategy Design, Process and Implementation in an Unstable/Complex Environment: A Second Exploratory Study Simulation Integration Contrasts Between MBAs and Undergraduates in the Capstone Policy Course An Investigation of the Real World Usefulness of a Strategy and Policy Course Using a Business Simulation Framework Duel (sic) Views of Internships, as Experiential Learning The Impact of Decision Support Systems on the Effectiveness of Small Group Decisions - An Exploratory Study An Investigation of the Relationship Between Formal Planning and Simulation Team Performance Under Changing Environmental conditions Sensitivity Analysis with the Complete IFPS/Personal Student Analysis Package: A marketing Decision Support System A New Approach to Teaching Salesmanship using Persona, Microskills, and a Sales Process A Rational Case for Synthetic Experience as a Prime Ingredient in the Marketing Curriculum SalesHire: A Microcomputer-Based Salesperson Selection Exercise TRANSECON: An Interactive Program for Learning Transportation Economics Hypercard as a Construction Tool for Short Instructional Exercises A Game to Introduce Accounting Information Systems Students to Certain Internal Control Concepts "Commitments" - A Demonstration Proposal An Analysis of Popular Games as Experiential Models for Corporate and Collegiate Management Education An Exploratory Study of the Effects of Strategic Emphasis in Management Games on Attitudes, Interest, and learning in the Business Policy Course Predicting Individual Decision Making Performance in a Business Simulation: An Empirical Study Strategic Planning And Organizational Performance In A Business Simulation: An Empirical Study, PAM (Planning Action Management) Simulation of a District Sales Territory Lifelong Learning and ABSEL: An Inquiry on Definitions and Relationships A Review of Salient Trends in Proceedings: A Fifteen year (1974 - 1988) Review of ABSEL Contributorship