the Effect of Affective Domain Characteristics on Behavioral or Psychomotor Outcomes Page 164 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 ABSTRACT Whole person learning (WPL) can be defined as the com- bined influences of the three learning domains, cognitive, affective, and psychomotor (behavioral) in experiential learning. Cognitive and psychomotor domains have long been studied and found to exert a great deal of influence as contributing to whole person learning. The idea of immer- sion (involvement) is important to whole person learning. In order for whole person learning to be at its greatest one must be immersed/involved in both body and soul. The diminishing of either one will diminish the degree of whole person learning that is taking place. When we consider the “emotional immersion” as part of the “whole person” learning outcome, we find that because emotions and feel- ing vary to a (great) degree, from individual to individual, so too can the sometimes negative effects (lesser influence) as well as the positive effects (greater influence) of emo- tions on how we feel toward something, and how that feel- ing affects not only how and how much we learn, but also to a degree what we learn. Therefore, the purpose of this study is to look at some factors in the affective domain and to determine what type of effect they have on whole person learning. INTRODUCTION In many aspects much of experiential learning is de- voted to what has been deemed “whole-person learn- ing” (WPL). This is where the cognitive, affective and psy- chomotor or behavioral dimensions of a person’s learning are addressed. That is, WPL always functions integrative- ly, combining the affective and behavioral domains with the cognitive domain always found in the educational pro- cess (Hoover, 1974). To be able to determine whether this type of learning has actually taken place, we can measure learning in each of these dimensions, singularly or in com- bination to arrive at a desired level of measure, across those dimensions. From this one could possibly assume that WPL either took place or it did not, in terms of absolutes and not to the degree. This can be seen or implied through Hoover, et.al. (2010) when it was asserted that, “although intended to produce meaningful outcomes, experiential exercises do not guarantee the integration of experiences across the cognitive, affective, and behavioral compo- nents”. It is however hard to imagine that these three com- ponents do not exist in some way, and to varying degrees, in every experiential exercise. The original divisions of learning outcomes; cognitive, affective and psychomotor were for the most part, arbitrary since psychologists and educators agreed that in teaching and real-life learning situations, no true separation of cog- nitive, affective and psychomotor states were possible (Bloom, 1956; Gephart and Ingle, 1976). While this re- mains true, these domains have been studied as separate entities in trying to best define whole-person learning (Gephart and Ingle, 1976; Krathwohl, Bloom and Masia, 1964). Of these components, the most studied is the cogni- tive, followed by the psychomotor/behavioral and then the affective. The reason for this is that it is much easier to study the cognitive (what people know) and the behavioral (their actions) than the affective (how they feel about or toward something). Also too, it has been shown by Giambatista and Hoo- ver (2010) that one of the keys to increasing the impact of experiential learning is through the (a) process that increase (s) the intensity of the experiential setting through a (the) process they labeled as “behavioral immersion”. This is the degree, they state, to which immersion takes place or exists is related to the degree to which the learner becomes “involved” or “engaged” in the exercise. The highest learning experiences are ones in which the learning individ- ual functions at a high level of arousal (awareness – a cog- nitive activity) and activity (performing behavior) on all dimensions (Hoover, 1974) or to be an active part of the exercise. This would, on the surface, seem logical because according to their proposed continuum (Figure 1) the di- mensions are contributory and perhaps even synergistically interactive (Hoover, 1974). What is not stated is that the affective domain and its effects are implied to be contrib- uting or influencing at their fullest, which at this point can only be assumed. It is proposed that the affective domain, being the least studied, and perhaps the most variable, has a range that influences the degree to which WPL occurs. This therefore is the purpose of this study. A question arises in that there is a direct absence of the affective domain. When one is involved or engaged in the exercise, the question that arises is this; “Is the learner en- gaged by “going through the motions” giving the desired behavior because that is what is expected of them, versus is THE EFFECT OF AFFECTIVE DOMAIN CHARACTERISTICS ON BEHAVIORAL OR PSYCHOMOTOR OUTCOMES Douglas L. Micklich Illinois State University dlmickl@ilstu.edu Page 165 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 the learner “truly passionate” about what they are doing. Emotions are generally thought of as rather destructive, and undesirable displays can be shown as stating, “Don’t get so emotional” or “ cool off and keep your head”. Emotion in this sense occurs within an individual and makes it difficult to relate to a situation in a predictable and acceptable man- ner (Russel and Black Jr., 1972). What is believed to be missing is the degree of ownership present (your emotions as they relate to the issue) because one can be involved (immersed) without taking ownership (just doing it to do it rather than displaying emotion) or being truly passionate about the issue. In other words, this is seen as being in relation to, as opposed to “doing without thinking” or “acting without thinking”. Since you (one) perceive(s) emotions as belonging to you (ownership), and you generate thoughts consciously, you (one) consider(s) the emotions to be part of a thought, not vice versa (and hence you call identified emotions, “thoughts”). Therefore it can be said, that a feeling is an- other word for unconscious thought and that emotion is an unconscious feeling; a feeling is like a conscious emotion (Pettinelli, 2011). Things that are emotional are things that cause you to think; consciously or unconsciously and there- fore they would cause you to feel, consciously or uncon- sciously. The more you like something (feel strongly about) and you can’t consciously identify as to why you like it (or do it), the more emotional it is. Emotion is a feeling, completely separate from facts or information (cognitive domain). Your intellect or ability to do things (behavioral domain) which are real is going to generate feelings just like emotions do (Pettinelli, 2011). From this we can now consider the issue of the existence of “affective or emotional” immersion where one puts their whole feel- ing, beliefs, attitudes, and values into the performance of the exercise and to what degree does this contribute to whole person learning. The affective domain, as stated earlier, is the least measured of the domains when it comes to whole person learning, yet, it is felt to be central to very part of the learn- ing and evaluation process. One of the reasons why inte- gration of the affective and cognitive domains has rarely been attempted is that affective behaviors (visible emo- tions) are difficult to conceptualize and evaluating cogni- tive behaviors are easier to specify, operationalize and measure (Martin and Briggs, 1986). Problems in identify- ing affective domain characteristics are that the concepts that comprise it are so broad and often unfocused that all aspects of behavior not clearly cognitive or psychomotor are lumped together in a category called affective (Martin and Briggs, 1986). This can be seen (is recognized) in the threshold of consciousness, with awareness and that of evaluation, with one’s willingness (based on emotional ties to the stimulus) to respond, is the basis for psychomotor responses. It is the bridge between the stimulus and the cognitive with psychomotor (behavioral) aspects of one’s personality. The purpose of this research is to examine the relation- ship(s) which may exist between the items in the affective domain and those of the behavioral or psychomotor. The dependent variables are those identified as those in the be- havioral domain with the affective domain items being in- dependent. Therefore, this research’s ain is to attempt to predict or determine the existence and the strength of the relationship that may exist between affective components on the behavioral or psychomotor domain. Therefore it is further hypothesized that; 1) that the stronger the relation- ship between affective variables and behavioral variables, the greater the degree of whole person learning takes place, 2) the strongest relationship should exist amongst all the variables. The point of concentration of this study focuses on the affective domain. If indeed it is central to every part of the learning and evaluation process, the end result will be seen in the conceptualization/evaluation of “non- discourse communication” in the psychomotor/behavioral domain. It is therefore further hypothesized that the strong- er the relationship between affective variables and behav- ioral, the greater the degree of whole person learning will take place. THEORETICAL DEVELOPMENT The basis for this lime of study stems from the two related facts; the first being that the affective domain is that which is least studied and the second is that aspects of be- havior not clearly cognitive or behavioral are lumped into the category called “affective”. This would more or les imply and inappropriately so, that the affective domain, in Figure 1 Conceptual Classification Scheme Illustrating Combinations of Experiential Learning Cognitive Affective Behavioral C/A C/B C/A/B High Intensity Null Null Null Yes Yes Definitely Learning Low Intensity Yes Maybe Maybe Possibly Possibly Possibly Page 166 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 most cases becomes the “catch-all” category where difficult or unexplained phenomena go. In this paper we are going to attempt to identify what makes up the affective domain and in identifying its characteristics, be able to attempt to predict the effects on the behavioral dimension. One of the reasons why integration of the affective and cognitive domains has rarely been attempted is that affec- tive behaviors are difficult to conceptualize and to evaluate cognitive behaviors are easier to specify, operationalize and measure (Martin and Briggs, 1986). Problems in identify- ing affective domain characteristics are that the concepts that comprise it are so broad and often unfocused that all aspects of behavior not clearly cognitive or psychomotor are lumped together in a category called affective (Martin and Briggs, 1986). This is with respect to how one individ- ualizes emotion and the relative importance one see in this respect. It has been measured to some extent, the existence of the relationship between these domains in determining the extent of whole person learning. One’s personality relies on how one defines how emo- tions rule our actions. In referring to definitions of emo- tions it implies that there is more than one way to under- stand emotion. An emotion is something of which we often are very much aware, and may interfere with the normal, rational way of behaving. Emotions are generally thought of as being destructive and undesirable displays which must be somehow controlled or concealed. As complex disturbances, they can also be thought of as an awareness of pleasantness or unpleasantness (Russell and Black, Jr., 1972). The presence of an emotion tends to give rise to a tension or drive toward or away from an object, situation, or person and obtaining this objective will satisfy that emo- tion and helps restore a balance. Emotions, like physical needs, act as drives to motivate the individual toward ac- tion (Russell and Black, Jr., 1972). A danger comes when emotions can be generalized across situations. Because emotions can also be unique to individuals and therefore cause individuals to react differ- ently, we should be cautious not to generalize with respect to a degree of learning. Furthermore, emotional or affec- tive behavior, while it may be appropriate for a woman, may be inappropriate for a man and it is likely in which men tend to overlook the role that this important source of energy plays in our daily behavior (psychomotor). It is becoming increasingly recognized that many of our deci- sions are made for emotional reasons rather than for ration- al or logical ones (Russell and Black, Jr., 1972). This in turn may affect the degree of whole person learning which takes place and the quality of such learning (is whole per- son learning different for males than for females). The question has also been posed as to whether a hu- man being even does thinking without feeling, acting with- out thinking, etc. Objectives and corresponding behaviors and evaluation materials differ in complexity and are usual- ly set for a given exercise. As the level of complexity changes, this original objective will become part of a fur- ther or subsequent objective such as the ability to apply (psychomotor connotation) the principles learned. It seems very clear, therefore, that each person responds as a “total organism” or “whole being” whenever they do respond (Krathwohl, et.al., 1964). In general, educators seem to desire to achieve the higher levels of affective goals in learners, including satisfaction in response and developing a system of values (Eiss, et.al., 1969). A closer examination of Eiss’s model (Figure 2) shows that cognitive activity occurs when the individual decides whether the stimulus is of interest, usually thorough exter- nal behavior’s sensory input to the subconscious. If the Figure 2 Eiss’s Model for Learning Page 167 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 level of interest demands further exploration then it prompts the individual to make a value judgment. This “value judgment can extend beyond pure curiosity to how much emotional attachment the person has.” Psychomotor responses can be of two forms; thinking and doing. In the thinking response, new information is stored in the individ- ual’s memory bank and we say that learning has taken place (Eiss, et.al., 1969). However the display of that learn- ing does not take place unless there is some active “doing” on the part of the learner. The intensity of the doing is hy- pothesized to reside in the degree of emotional or affective immersion related to the beliefs, attitudes, and values of the learner. Therefore, the greater the intensity of the emotion, the greater will be the intensity of the behavior (doing). If it is true that one can surmise that a person’s value system contributes to their behavior, it then stands to rea- son that how a person behaves and to the extent of that be- havior lies in the level of understanding and associative feelings they already have and continue to develop over time, either by individual exposure or by group pressure. Therefore, in examining the affective domain we seek to determine if various characteristics or aspects adds to the existing relationship. It is hoped that, in general, it adds to the relationship so that one can experience greater whole person learning. Various aspects of the affective domain from a motivational standpoint, one recognizes that attrib- utes and values are things that drive us to act. Given that, it is possible to design/develop motivational tools to help us and not only to act, but to act with greater conviction based on how these individual aspects influence/moderate/ mitigate the relationship, and that the conviction may/will lead to increased performance and greater degrees of learn- ing. In further consideration of the role of emotions, Petti- nelli (2011) state that thoughts are separate from emotions because thought is a period of thinking, and that there is an overlap of feeling and thought (refer to current proposed model of ABSEL thinking). There are still parts of thought that don’t have feeling or emotion in them (thinking with- out feeling), and there are parts of emotion that do not have thoughts in them (doing without thinking). If you are going to be emotional, you are going to be less attentive to some- thing that you would be if you were thinking more. This would also depend upon what you are thinking about at the time in regards to certain stimuli (awareness in the cogni- tive domain). If you feel that the stimulus is good, then you are going to give it more attention than if you feel that it is bad (possibly). This in turn can affect the degree of whole person learning. Furthermore, Pettinelli states that thought and feeling may result in the same amount of attention to something, but thought is more precise, with emotions and feelings being more obscure (and hence harder to measure and de- termine their effect on behavior, which is one of the rea- sons why they have not been studied much) Emotions are thoughts you can’t identify or are difficult to do so. When you feel something, it must be that you are thinking about something (or a particular thing) unconsciously, you just have no idea what it is. Emotions cannot generate thoughts by themselves, but they can drive behaviors. The desired outcome is to have the greatest degree of whole person learning, and in a sense, to minimize the “credibility gap” which may exist in the psychomotor/ behavioral domain (Eiss, et.al., 1969). This learning expe- rience is seen through Eiss’s Model for Learning (Figure 2) which shows the relationships amongst the domains and their contributions to learning. In order to get to that stage or point of whole person learning, we must be able to pin- point and identify with some degree of accuracy, those characteristics associated with one’s feelings, attributes and beliefs, and be able to motivate and properly channel those synergies as they contribute to learning (this is why the feedback loop affects the affective domain, as opposed to affecting either the cognitive or behavioral). What is hoped to be shown is the following:  The existence of a relationship between and among the dimensions proposed in Eiss’s (1968) Model for Learning.  The possible existence of high intensity learning as depicted in Hoover’s (1974) conceptual classification structure.  The existence of low intensity learning in experiential exercises. Figure 3 Proposed Model of Current ABSEL Research Page 168 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 The intensity of learning (behavior) is hoped (can be) shown through a moderate correlation strength relationship between the variables as well as the overall strength. We will begin the investigation by looking at the be- havioral dimensions as the dependent variable (cognizant of the individual aspects) in relation to the cognitive dimen- sion. The independent variable in this case will be the af- fective domain characteristics. We will then look at each affective characteristic individually and then at the total relationship to each behavioral dimension characteristic. This should give us an overall feel for the total extent of the relationship. Much of the (current) ABSEL research on whole person learning uses a model similar to the one in Figure 3, where we think about what it is that we are trying to experience and learn and then go out and do it. The dotted/dashed line represents this current line of thought. The addition of feelings, values and attitudes as a direct line/relationship to the model adds the value of feel- ing. A feeling of emotion helps direct what we do, in the behavioral realm and in some way how we do it. In other words, the passion we undertake and its contribution to the learning process. The model shows that much of the re- search either by passes the affective domain altogether or treats it lightly in assuming its effect on overall learning. This would be consistent with Martin and Briggs (1986). METHODOLOGY Students (n=60) in an introductory management course were divided into groups of four by self-selection. Stu- dents stayed in these groups for the duration of the course. A series of nine experiential group exercises were adminis- tered over the course of the semester. A survey was admin- istered across the three dimensions, cognitive, affective and behavioral. The characteristics which comprised the cogni- tive domain are listed in Table 1. Cognitive domain varia- bles are those which require an intellectual awareness that stimulates the thought process. Affective domain items (Table 2) address our emotions of how we feel toward the stimulus. The greater the feeling or emotional ties we have concerning these variables, the greater the ownership, and hence the more apt we are to find that these have an effect on how we behave and/or learn. Psychomotor or behavior- al dimension items (Table 3) address our actions and how we carry out our emotions. These variables are observable outcomes of the learning process. Determining if there is a direct relationship in which of the affective variables has the greatest influence or effect on the behavioral variables. A linear regression model was used to investigate the relationships which existed among the domains, primarily the affective and behavioral. Six different models sur- Table 1 Cognitive Domain Variable Name Description Helplearn Helped me learn new things Encthink Encouraged me to think about the material IndentProb Helped my ability to identify problems HelpIntr Helped me to see integration of the course material Table 2 Affective Domain Variable Name Description DrawRel Helped me see or draw relationships between topic areas AnalProb Helped my ability to analyze problems ThinkCreat Increased my ability to think creatively UnderstdAbil Helped understand my own abilities KnlBusPrn Helped my knowledge of business principles SelfConfid Helped in developing my self-confidence Page 169 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 rounding the items in the behavioral dimension (Table 3), and using items from Table 2 as independent variables were constructed. For each of these a regression analysis was run yielding the results shown in Tables 4 – 9. These results yielded the following observations: the variables listed in the model were as a result of those items being significant in contributing to the overall relationship; there were either two or three variables identified; many of the variables appeared more than once throughout the analysis. The number of appearances of these variables is listed in Table 10. As you can see strong relationships existed for R and R2 for each of the dependent variables and that those rela- tionships increased in strength as the number of variables increased. It was also found that no model contained more than three independent variables. The Beta (standardized) values were also looked at in order to determine the influ- ence that each variable had in the total relationship. Stand- ardized Betas were used because they allow for direct com- parison of the relative strength of the relationships between variables. Beta values because they allow for direct com- parisons of the relative strengths of the relationships be- tween variables. Beta values are between +1 and -1, a par- tial correlation show between two variables in which the influence of all other variables has been partialed out. Therefore, it is the unique contribution of one variable to explain another variable. In each of the models it was found that there was an increase in the number of independent variables and that in each case of three predictor variables did the Beta initially increase and then decrease. In the case of only two predic- tor variables was shown decreases in the Beta. It is inter- esting to note that in each case, between cases , the influ- ence of a particular variable was different in each case. An example would be between the variables in Table 7 (Increase effectiveness in other business courses) and Table 5 (Can help me become a more effective manager). Each of these had the same predictor variables, but in Table 7 you could find the influence of SelfConfid being less than SelfConfid in Table 5. This would indicate that emotion plays a larger role in being a more effective manager than it would in how well you perform or would perform in other business courses. In the case of models with three predictor variables Table 3 Behavioral/Psychomotor Domain Variable Name Description HelpApply Help me apply what I learned in class discussion ApplyTech Helped my ability to apply techniques MakeDec Helped my ability to make decisions WorkPeople Helped me work with people IncEffect Would help increase my effectiveness in other business courses Effec Mgr Can help me become a more effective manager Table 4 (n=60) Relationship between Psychomotor and Affective Dimensions: ApplyTech Dependent Predictor Predictor Predictor Variable Measure Variable Variable Variable ApplyTech DrawRel ThinkCreat Undstdabil R .842 .911 .923 R2 .710 .830 .852 Adj. R2 .705 .825 .844 Beta (standardized) .842 .494 .385 .492 .408 .231 Page 170 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 Table 5 (n=60) Relationship between Psychomotor and Affective Dimensions: EffecMgr Dependent Predictor Predictor Variable Measure Variable Variable EffecMgr Knlbusprin Selfconfid R .762 .855 R2 .581 .731 Adj. R2 .573 .880 Beta (standardized) .762 .492 .473 Table 6 (n=60) Relationship between Psychomotor and Affective Dimensions: HelpApply Dependent Predictor Predictor Predictor Variable Measure Variable Variable Variable HelpApply DrawRel AnalProb Knlbusprn R .842 .889 .902 R2 .709 .790 .783 Adj. R2 .704 .783 .804 Beta (standardized) .842 .548 .354 .409 .373 .271 Table 7 (n=60) Relationship between Psychomotor and Affective Dimensions: IncEffect Dependent Predictor Predictor Variable Measure Variable Variable IncEffect Knlbusprn SelfConfid R .715 .795 R2 .511 .632 Adj. R2 .502 .619 Beta (standardized) .715 .472 .425 Page 171 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 Table 8 (n=60) Relationship between Psychomotor and Affective Dimensions: MakeDec Dependent Predictor Predictor Predictor Variable Measure Variable Variable Variable Make Decision Analprob DrawRel ThinkCreat R .815 .878 .887 R2 .665 .770 .787 Adj. R2 .659 .767 .776 Beta (standardized) .815 .480 .319 .467 .410 .243 Table 9 (n=60) Relationship between Psychomotor and Affective Dimensions: WorkPeople Dependent Predictor Predictor Variable Measure Variable Variable Work People ThinkCreat SelfConfid R .737 .827 R2 .544 .684 Adj. R2 .536 .673 Beta (standardized) .737 .504 .441 Table 10 (n=60) Appearance of Predictor Variables Variable Name Appearance DrawRel 3 ThinkCreat 3 Undstdabil 1 Knlbusprn 3 SelfConfid 3 AnalProb 2 Page 172 - Developments in Business Simulation and Experiential Learning, volume 39, 2012 (Table B; the ability to make decisions and Table 6, Help me apply what I learned in class discussions), we see that the Beta coefficients as first increasing and then decreasing, thus indicating a decreased influence of the third variable (Knlbusprn and Thinkcreat respectively) on the strength of the relationship. This would also give validity to the differ- ences in R and R2 from AnalProb and Knlbusprn in Table 6 and DrawRel and ThinkCreat from Table 8. In these two models it is also interesting to note that the predictor variables DrawRel and AnalProb are the first two variables and their positions are switched in each mod- el. The effect of these two variables, when switched, shows a lesser of an influence (difference) when being able to make a decision. (,013; MakeDec) versus helping to ap- ply the techniques (.139); HelpApply). When all three var- iables are considered we first see an increase in the influ- ence and then a decrease in influence. This would indicate that different emotions affect our behavior differently and at different times. For those models with only two variables, we see a decrease in the influence of the second variable with an associated increase in the strengthening of the relationship. In this manner, it can be said that different emotions will exhibit different effects on the behavioral outcomes on ex- periential learning. IMPLICATIONS FOR FUTURE RESEARCH What can be implied by this research is that emotions do have an effect in experiential learning and in particular “whole-person” learning. The degree to which we are im- mersed cognitively will have a (purely) positive effect on that learning. Although not explicitly studied here has a potential impact for/in a future study. However, when we consider the “emotional immersion” as part of the “whole person” learning outcome, we find that because emotions and feelings vary to a (great) degree, from individual to individual, so too can the sometimes negative effects (lesser influence) as well as the positive effects (greater emotion) of emotions and how we feel toward something. Therefore “whole-person” learning, as we know it, really “whole person learning” and can we have a greater whole, by addressing ones attitudes within the learning. Addition- ally, is whole person learning the same for males as for females. REFERENCES Bloom, Benajmin S., Max D. Engelhart, Edward J. Furst, Walker H. Hill, David R. Krathwhol, Taxonony of Educational Objectives: The Classification of Educa- tional Goals: Handbook 1: Cognitive Domain, David McKay Company, Inc. (1956) Eiss, Albert F., Mary Blatt Harbeck, Behavioral Objecitves in the Affective Domain, National Science Supervisors Association, 1969 Gephart, W. J., & Ingle, R.B., (1976) Education in the Af- fective domain, Proceedings of the National Symposium for Professors of Educational Research (NSPER), Phoenix, Arizona Harrow, Anita J., A Taxonomy of the Psychomotor Do- main: A Guide for Developing Behavioral Objectives, David McKay Company, Inc., New York, 1972 Hoover, J. Duane, Experiential Learning: Conceptualiza- tion and Definition, Simulation, Games, and Experiential Learning Techniques, vol. 1, 1974 Hoover, J. Duane, Robert C. Giambatista, Ritch L. Sorenson, William H. Bommer, Assessing the Effectiveness of Whole Person Learning Pedagogy in Skill Acquisition, Academy of Management Learning and Education, 2010, Vol. 9, No. 2, 192-203: Krathwohl, David R., Benajmin S. Bloom, Bectram B. Masia, Taxonomy of Educational Objectives: Handbook 2: Affective Domain, Longman Inc., New York, 1964 Martin, Barbara L., Leslie J. Briggs, The Affective and Cognitive Domains: Integration for Instruction and Research, Educational Technology Publications, Eng- lewood Cliffs, New Jersey, 1968 Pettinelli, M. (2011, October 10) The Psychology of Emo- tions, Feelings and Thoughts. Retrieved from the Connexions Website: http://cnx.org/content/col10447/115/ Russell, G. Hugh, Kenneth Black Jr., Human Behavior in Business, Prentice Hall, Inc., Englewod Cliffs, New Jersey, 1972, pp 148-152 Table of Contents Volume 39, 2012 Designing the Training Challenge Follow The Leader: Are we Teaching our Students to be Thinkers or Followers? Two Free-Rider-Accepting Methods of Organizing Groups for a Business Game Additional Benefit Through Competency Models Assessing Brand Portfolio Normative Consistency & Trends With The Normative Position of Brands & Trends Package Modeling the Impact of Marketing Mix on the Diffusion of Innovation in the Generalized Bass Model of Firm Demand Play it Forward! The Design and Development of a Forward Contract Simulation Positioning the Company: Increasing Profits in Social Networks Merger of Companies in Business Game Exercise Towards a Knowledge-Based Approach for Autonomouse Trading Agent An Exploratory Study of the Impact of a Simulation Exercise on the Managerial and Personality Traits and the Decision Making Styles of Marketing Students Should the Concept of Potential Customers be the Foundation of Demand Theory in Business Simulations? Teaching Sustainability Experientially Drawing Upon Experience and Research to Improve Future Communications Improving Assessments of Student Learning Outcomes (SLO) Over Time The Effect of Affective Domain Characteristics on Behavioral or Psychomotor Outcomes Gossip? No, Not Me! An Experiential Exercise Student Advisement Using Gantt Charts: An Experiential Exercise in Management Theory Practicing Teachers as Digital Game Creators: A Study of the Design Considerations Designing and Solving Crossword Puzzles: Examining Efficacy in a Classroom Exercise Difficult Times Call for Innovative Measures: Microfinance as Experiential Learning in Higher Education Catalysts, Client Services, and Community Change: Interdisciplinary Collaboration in a Nascent Microfinance Initiative Build A Business . . . In An Hour or Less: Getting Closer to Reality into the Classroom Smart Goals: How the Application of SMART Goals can contribute to achievement of Student Learning Outcomes The Use of Data in "Live" Cases to Encourage Systems Thinking and Integrative Analysis: An Exercise Linking Human Resource Programs and Financial Outcomes in Real Organizations Experiential Education as a Process of Changing Mental Frames by Inducing Insight Learning Process and Content Integration in an Experiential Learning Guided Internship Program Good-bye Discussion Thread: Creating a Community of Inquiry in an Online Master's Program Fiction as a Constructivist Tool for Learning Process Consultation in an Online Environment: Shaping the Context, Introducing the Dialogue Can Simulations Provide a Better Experience? A Capstone Application Modeling a Modest Proposal for Increasing the Efficiency of Academic Reserarch Dissemination Experience GEO: A Massively Multiplayer Game SysTeamsGames Three Games for Management Simulation SimVenture - A Start-Up Business Simulation Stellarbucks Simulation Developing Games Using Strategy Maps and Balanced Scorecards Strategy Dynamics Models - Powerful But Simple In-Class Games Simulating Scenarios for Financial Statement Analysis A Valuation Model of the Simulated Firm Writing the Land: An Interdisciplinary Experiential Approach On the Estimation of the Probability of Meeting Financial Commitments: A Behavioraial Finance Perspective Using Business Simulations