7. Learning and Development in Higher Education

. . . the ways in which we believe and expect have a tremendous effect on what we believe and expect. We have discovered at last that these ways are set, almost abjectly so, by social factors, by tradition and the influence of education. Thus we discover that we believe many things not because the things are so but because we have become habituated through the weight of authority, by imitation, prestige, institution, and unconscious effect of language, etc. We learn, in short, that qualities which we attribute to objects ought to be imputed to our ways of experiencing them, and that these in turn are due to the force of intercourse and custom. This discovery marks an emancipation; it purifies and remakes the objects of our direct or primary experience.

—John Dewey, Experience and Nature

From the perspective of experiential learning theory, educational institutions are the curators of social knowledge. Chief among the responsibilities of their curatorship is the creation of conditions whereby social knowledge is made accessible to individual learners for their development. Jerome Bruner has underscored the importance of this task:

Perhaps the task of converting knowledge into a form fit for transmission is, after all, the final step in our codification of knowledge. Perhaps the task is to go beyond the learned scholarship, scientific research, and the exercise of disciplined sensibility in the arts to the transmission of what we have discovered. Surely no culture will reach its full potential unless it invents ever better means for doing so. [Bruner, 1971, p. 19]

This responsibility includes the individual development of participants in these institutions in all three developmental stages of experiential learning: (1) acquisition, the preparation of individual learners in basic skills so that they can access and utilize the tools of social knowledge; (2) specialization, the selection and socialization of learners into specialized areas of knowledge that suit their talents and meet societal needs; and (3) integration, the development of the unique capabilities of the whole person toward creativity, wisdom, and integrity. The first of these responsibilities has traditionally been the province of primary and secondary education, although the increased knowledge necessary to function effectively in modern society, coupled with the general decline in the public educational system, has caused considerable overflow of this responsibility into the system of higher education.

The third educational responsibility of integrative fulfillment has for many years been suffering at the expense of the second, specialized occupational training. The selection of Charles Eliot as president of Harvard in 1869 marked the end of classical education in American colleges whereby all students took the same courses in Greek, Latin, and mathematics. By introducing electives and “majors” in the Harvard curriculum, he began what, considering the rapid growth of knowledge, was the inevitable specialization and fragmentation that characterizes the modern university. In the system that has emerged in the last 100 years, students have been increasingly free to select their courses and to define programs suited to their needs, interests, and abilities. Academic disciplines have enjoyed a corresponding freedom to choose those students who best fit their requirements. This trend toward specialization and vocationalism in higher education has recently gained momentum from post-“baby-boom” demographics, a tight job market, and the multifaceted financial crises of institutions of higher learning. College and universities are thus increasingly specialized and fragmented, held together by little more than Robert Hutchins’s central heating system, Warren Bennis’s parking authority, and a few distribution requirements. In a highly complex and specialized society, the pressures toward specialization in education feed on themselves. Higher education is increasingly called upon to deliver the specialized knowledge, skills, and attitudes needed for students to find their niche in society, and to service that niche as well. Institutions, in turn, become increasingly dependent on these “social niches” for their own survival, and lend further support to the forces of knowledge specialization, usually at the expense of integrative education. Speaking of the specialized formal educational requirements required for entry in occupations, Robert Hutchins said:

The great bulk of the students in American universities are there in order to meet these requirements. The public acquiesces in them, first, because it is accustomed to acquiesce in the demands of pressure groups, and second because it has a vague feeling that the members of certain occupations at least should be certified, or sanctified, in some way before they are let loose upon the public. The public is unwilling, often for good reasons, to trust these occupations to certify their own members. The universities acquiesce in these arrangements because they wish to increase their enrollments; students bring in income, and anyway there is a general feeling that excellence in educational institutions, as in most other things, increases in proportion to size. The trades, occupations, businesses, and professions promote these arrangements in order to restrict competition and enhance their prestige. [Hutchins, 1953, p. 31]

Before we return to a consideration of integrative development, it is important to understand first the consequences of this specialized emphasis of higher education on student learning and development. In considering the student careers that are spawned and shaped in the university community and the university’s responsibility for the intellectual, moral, and personal development of its members, we have often emphasized the unitary linear trend of human growth and development at the expense of acknowledging and managing the diverse developmental pathways that exist within different disciplines and professions. These paths foster some developmental achievements and, as we shall see, inhibit others. The channels of academic specialization are swift and deep, the way between them tortuous and winding. Many years ago, I served as a freshman advisor to undergraduates in a technological university. Two or three of my students in each group faced the awkward realization near the end of their freshman year that a career in engineering was not quite what they had imagined it to be. What to do? Transfer to a liberal arts school and possibly lose the prestige of a technological education? Endure the institute’s technological requirements and “bootleg” a humanities major? Switch to management? Most decided to wait and see, but with a distinct loss of energy and increase in confusion. I felt powerless about what to advise or even how to advise.

It was only later that I was to discover that these shifts represented something more fundamental than changing interests—that they stemmed in many cases from fundamental mismatches between personal learning styles and the learning demands of different disciplines. That disciplines incline to different styles of learning is evident from the variations among their primary tasks, technologies, and products, criteria for academic excellence and productivity, teaching methods, research methods, and methods for recording and portraying knowledge. Disciplines, as we have seen, show sociocultural variation—differences in faculty and student demographics, personality, and aptitudes, as well as differences in values and group norms. For students, education in an academic field is a continuing process of selection and socialization to the pivotal norms of the field governing criteria for truth and how it is to be achieved, communicated, and used, and secondarily, to peripheral norms governing personal styles, attitudes, and social relationships. Over time, these selection and socialization pressures combine to produce an increasingly impermeable and homogeneous disciplinary culture and correspondingly specialized student orientations to learning. This chapter will explore the dynamics of this specialized developmental process in undergraduate and professional education.

Whatever the characteristics of an individual that selectively propel him toward particular educational settings—going to college, selecting a particular one, choosing a certain academic major, acquiring membership in a particular group of peers—these same characteristics are apt to be reinforced and extended by the experiences incurred in those settings. [Feldman and Newcomb, 1969, p. 333]

Thus, if students with a particular learning style choose a field whose knowledge structure is one that prizes and nurtures their style of learning, then accentuation of that approach to learning is likely to occur. The result is an educational system that emphasizes specialized learning and development through the accentuation of the students’ skills and interests. Students’ developmental pathways are a product of the interaction between their choices and socialization experiences in academic fields such that choice dispositions lead them to choose educational experiences that match these dispositions, and the resulting experiences further reinforce the same choice disposition for later experiences.

Some examples will serve to illustrate this process of specialization in learning style as a result of accentuation. In a first attempt to examine the details of this process, Plovnick (1971) studied a major university department using the concept of convergence and divergence defined by Hudson (1966). He concluded that the major emphasis in physics education was on convergent learning. He predicted that physics students who had convergent learning styles would be content with their majors, whereas physics majors who were divergent in their learning styles would be more uncertain of physics as a career and would take more courses outside the physics department than would their convergent colleagues. His predictions were confirmed. Those students who were not fitted for the convergent learning style required in physics tended to turn away from physics as a profession, while those physics students having a convergent style tended to continue to specialize in physics, both in their course choices and their career choices.

In another unpublished study, we examined the accentuation process as it operated at the molecular level of course choice. This research examined the choice of sensitivity training by graduate students in management. When we gathered the Learning Style Inventory (LSI) scores of students who chose a voluntary sensitivity-training laboratory, we found that they tended to be more concrete and reflective than those who chose not to attend the lab. When those with divergent learning styles completed the training sessions, their LSI scores became even more concrete and reflective on a post-test, accentuating their disposition toward divergent learning experiences.¹

1. Another set of questions is raised by the choice/experience cycle in development. Although the results of these studies show that the majority of students seem to be involved in a series of choices and experiences that accentuate their learning-style tendencies, many students deviate from this dominant trend. If we are to understand the role of learning styles in the development process, we need to understand not only dominant trends but also the causes for deviation from these trends. More specifically, we may gain from this kind of analysis more insight into the relative importance of choices and experiences in human change and development. Until now, much emphasis has been placed on the primary importance of experience as the cause of change. This orientation has given rise to countless research studies seeking to measure with before-after change measurements the effects of various educational experiences. But suppose that a person was changed as much by his choices of experiences as by the experiences themselves. Take, for example, a person interested in mathematics. His interests and aptitudes in mathematics may lead him to seek educational experiences that will enhance these dispositions. In addition, he may be screened formally and informally for admission to this educational program, gaining entry only if he has mathematical aptitude. Thus, by the time his choice has been realized, he will already (1) have gone through a process of consciously recognizing an aspect of himself that he wanted to develop (i.e., his mathematical ability); (2) have done some planning about how to develop this aspect; and (3) perhaps have begun learning mathematics in order to pass selection tests. All these processes will have occurred before the “before” measurement in the typical study designed to assess the effects of an educational experience. Yet they may be as important in determining the directions of development.

Witkin and his associates (Witkin, 1976) have shown that global (field-dependent) students choose specializations that favor involvement with people—such as teaching, sales, management, and the humanities—whereas analytical (field-independent) students choose areas that favor analysis, such as the physical sciences, engineering, and technical and mechanical activities. Clinical-psychology graduate students tend to be global, and experimental-psychology graduate students are analytical. In addition, Witkin found that when cognitive style matches the demands of a given career specialization, higher performance results.

It is important to note not only that the content of choices is associated with cognitive style, but also that there is an association between the choice process and cognitive style. Thus, global students make choices preferred by their peer group, whereas analytical students are more likely to use systematic planning and goal setting. Plovnick (1974) found a similar pattern when he used the LSI to study medical students’ choice of medical specialty. There were significant relationships between the LSI scores and specific choices made: accommodators chose medicine and family care; assimilators chose academic medicine; divergers chose psychiatry; and convergers chose medical specialties. In addition, LSI scores were related to the process of choosing: concrete students tended to base their choices on role models and acquaintances, abstract students relied on theoretical material and interest in subject matter.

Robert Altmeyer (1966) has dramatically illustrated the result of the accentuation process on cognitive abilities in his comparative study of engineering/science and fine-arts students at Carnegie Tech. In a cross-sectional study, he administered two batteries of tests to students at all levels in the two schools; one battery measured analytical reasoning, the other creative thinking. As predicted, engineering/science students scored highest on analytic reasoning and fine-arts students highest on creative thinking; and over the college years, these gaps widened: engineering/science students became more analytical and fine-arts students more creative. The surprising finding was that engineering/science students decreased in creative thinking and fine-arts students decreased in analytic reasoning over the college years. Thus, educational processes that accentuated one set of cognitive skills also appeared to produce loss of ability in the contrasting set of skills.

The corollary to the accentuation process of development in which skills and environmental demands are increasingly matched is the alienation cycle that results when personal characteristics find no supportive environment to nurture them. In this emerging information society, severe alienation can result when there is an incongruity between personal knowledge and social knowledge. This is illustrated most dramatically by the alienation of the poor, whose streetwise way of learning doesn’t fit with the symbolic/technological knowledge of the university; or more subtly, it is illustrated by the creative writer who is “turned off” by the pedantic critical climate of her English literature department, or the adult who returns to college and finds little recognition for a lifetime of learning by experience.

2. For a detailed report, see Kolb and Goldman, 1973.

Data for the study (except for cumulative grade averages, which were obtained from the registrar’s office) were collected by means of a questionnaire that was sent to the 720 TECH seniors two months before graduation. Four hundred and seven students (57 percent) responded to the questionnaire. Of these responses, 342 (43 percent) were complete enough to test the hypotheses in this study. The questionnaire included the Learning Style Inventory, two scales measuring political alienation and anomie, questions about plans for next year, career choice, degree of commitment to that career, undergraduate major, perception of academic workload, and involvement with peers. These variables will be described in detail as the results are presented.

Figure 7.1 shows the LSI scores of students with different departmental majors in those departments with ten or more students. Analysis of variance for the six learning-style dimensions by departmental major shows that reflective observation, active experimentation, and the combination score active-reflective all vary significantly by departmental major. Differences on the abstract-concrete dimensions show no significance. This lack of significant differentiation may well be because of more uniform selective and normative pressures toward abstraction that operate across all the university departments. TECH’s reputation as a scientific institution is strongly based on scholarship and the advancement of scientific knowledge. Humanities, architecture, and management are the most concrete departments in the university, and our observations would indicate that these are all quite scholarly in comparison with more concrete programs in other, less “academic” schools such as fine arts, drafting, or business administration. Selective and normative forces on the active-reflective dimension are more diverse, representing the tension in the university between basic science and practical application. With the exception of electrical engineering, the engineering departments are the most active in the university. With the exception of chemistry, the basic sciences and mathematics are more reflective.

When the pattern of relationships among departments at TECH is compared with the Carnegie Commission data representing colleges and universities of all types (Figure 5.4), one is immediately struck by the fact that with the exception of architecture and humanities, the concrete disciplines in Figure 5.4 are not represented at this university (there are philosophy, political science, and psychology departments, but they had only two or three students each in our sample). Otherwise, there is a general correspondence with previous studies. Humanities falls in the diverger quadrant, mathematics is assimilative, and management is clearly accommodative. Although the engineering departments all fall on the lower edge of the accommodator quadrant rather than the converger quadrant as predicted, this is most likely a function of the general abstract bias of the university as a whole. Physics and chemistry are not as abstract and reflective as predicted, although if the LSI scores of only those students planning to attend graduate school are used (as indicated by the arrowheads in Figure 7.1), the pattern is more consistent with prediction. Economics is somewhat more abstract and active than the Carnegie data, although, as we will describe later, this is probably a function of the unique nature of the TECH department. The architecture department’s position in the divergent quadrant is also to some extent a function of the unique nature of the department, with its emphasis on creative design and photography as well as the more convergent technical skills of architecture.

The results above should be viewed as only suggestive, since several measurement problems prevented a more accurate test of the accentuation hypothesis. The first problem was that it was difficult to determine whether or not a mathematics student planning graduate work in artificial intelligence would continue studying mathematics. Even though most students clearly planned graduate training in the field of their major, the few borderline cases do contaminate the results. A second measurement problem lies in the fact that graduate study in general for TECH students is associated with an abstract and active orientation. Since all six of the departments that did not follow the accentuation prediction showed a tendency toward abstractness, and four of the six showed a tendency toward the active orientation, this general tendency for graduate study may well have overshadowed the accentuation process in those departments. The final measurement problem has to do with the prediction of learning demands for those departments like electrical engineering whose students score close to the middle of the LSI grid.

To deal with these problems in the measurement of the accentuation process, four departments were selected for more intensive case study. Several criteria were used to choose departments whose learning-style demands matched the four dominant learning styles. The first criterion used the average learning-style scores of the students in a given department as an indicator of the learning-style requirements of that department. This criterion assumes that on the average, students will over their college careers select themselves and be selected into fields that match their learning styles. The criterion clearly identified three TECH departments that matched three of the learning types—humanities was divergent in its learning demands, mathematics was assimilative, and economics was convergent (see Figure 7.1).

The fourth department that was ultimately chosen, mechanical engineering, was accommodative but was not clearly different from other departments in the accommodative quadrant. To pick the most representative accommodative department, three other criteria were applied. The first was to pick a department whose students going to graduate school showed an accentuation of the departmental learning style. Three departments in the accommodative quadrant showed this accentuation process—chemical engineering, mechanical engineering, and management—as did the three departments already chosen to represent the other styles. Of these three accommodative departments, chemical engineering seemed most representative, but we had to eliminate it because all but two of the students in the department had accommodative learning styles. This made impossible comparisons between students who matched the departmental norms and those who did not. The other candidate, management, was eliminated because a closer examination of students in that department showed that it comprised two separate and distinct groups: behavioral-science and management/computer-science majors. Thus, students would not be reacting to a single set of departmental learning-style demands.

As a final check, the educational objectives and curricula of the four departments selected by the criteria above were examined for indications of their learning-style demands. Humanities and mathematics showed strong indications of divergent and assimilative orientations, respectively, as our previous data and theory would predict. For example, course descriptions in humanities often emphasize “different perspectives” of a literary work. In mathematics, the emphasis is on basic theory and research, as this quote from the TECH Bulletin’s description of the undergraduate mathematics program indicates:

The immediate educational aims are to provide an understanding of a substantial part of the existing body of mathematical knowlede and an ability to impart this knowledge to others. But most important, the department hopes to inspire a deep interest in the discovery or invention of new mathematics or interpretation of mathematics to a new field.

By indication of the learning styles of its students, the economics department at TECH is considerably more convergent—abstract and active—than economics majors in our previous research (Kolb, 1973). This convergent emphasis is borne out, however, by the objectives and curricula of the department. The department places a very strong emphasis on the quantitative/theoretical and policy-formation aspects of economics and considerably less on the more liberal-arts approach (for example, economic history).

Although our previous work showed that engineers on the average fall in the convergent quadrant of the LSI, we were able to obtain no differentiation among the various forms of engineering. One advantage of studying a technical institute like TECH is that we can begin to differentiate among these types. One would expect, for example, that mechanical engineering, with its relatively small theory base, would be more concrete than electrical engineering, where theory plays a larger role. The concrete orientation of mechanical engineering can be illustrated by the following quote, excerpted from the TECH Bulletin description of undergraduate study in mechanical engineering:

. . . the student must experience the ways in which scientific knowledge can be put to use in the development and design of useful devices and processes. To teach this art, largely by project-oriented work of creative nature, is the primary object of subjects in laboratory and design.

To study the career choices of the students in the four departments, we used each student’s LSI scores to position him/her on the LSI grid with a notation of the career field he/she had chosen to pursue after graduation. If the student was planning to attend graduate school, the career field was circled (the results of this analysis are shown in Figures 7.2 through 7.5). If the accentuation process were operating in the career choices of the students, we should find that those students who fall in the same quadrant as the norms of their academic major should be more likely to pursue careers and graduate training directly related to that major, while students with learning styles that differ from their discipline norms should be more inclined to pursue other careers and not attend graduate school in their discipline. Although the sample size is small and most students plan some form of mechanical-engineering career, this career-choice pattern can be seen in the mechanical-engineering department (Figure 7.2). All four of the students in the accommodator quadrant (100 percent) plan careers in mechanical engineering and graduate training as well. Only four of the ten students (40 percent) whose learning styles do not fit mechanical engineering are committed both to straight engineering careers and graduate training. The pattern is more clear in the mathematics department, where we have a somewhat larger sample (Figure 7.3). Ten of the thirteen mathematics students (80 percent) whose learning styles are congruent with departmental norms choose careers and graduate training in mathematics. Only two of the thirteen students (15 percent) whose learning styles are not congruent plan both careers and graduate training in math (these differences are significant using the Fisher Exact Test, p < .01). Figure 7.4 shows the same trend in the economics department, although to a lesser degree. Three of the six economics students (50 percent) with congruent learning styles plan graduate training and careers in economics, but only one of the six (17 percent) with different learning styles has such plans.

The pattern in the humanities department (Figure 7.5) is somewhat more difficult to interpret. One is immediately struck by the fact that only three of the eleven students (27 percent) in the humanities department plan to attend graduate school. This is in contrast to the fact that 63 percent of all TECH seniors plan graduate training. In addition, all of the humanities students’ career choices are somewhat related to the humanities but are definitely unrelated to the core curricula of TECH. In this sense, the humanities department as a whole seems not to fit with the learning demands of the rest of the institution. The concrete/reflective orientation of humanities seems in conflict with the abstract and active orientation of a technical school. We will explore this hypothesis further in the next section of results on performance and adjustment at TECH.

To further test the accentuation process in the four departments, we examined whether the student’s choice-experience career-development cycle, indeed, operated as an accentuating positive feedback loop. If this were so, then those students whose learning-style dispositions matched and were reinforced by their discipline demands should show a greater commitment to their choice of future career field than those whose learning styles were not reinforced by their experiences in their discipline. As part of the questionnaire, students were asked to rate how important it was for them to pursue their chosen career field. They expressed their answers on a 1-5 scale, where 5 equaled “great importance.” The average ratings for students whose learning styles matched discipline demands and those whose styles did not match the norms of their discipline are shown for the four departments in Figure 7.6. In all four departments, the average importance rating was higher for the students with a match between learning style and discipline norms (the differences being statistically significant in the mechanical-engineering and economics departments). Thus, it seems that learning experiences that reinforce learning-style dispositions tend to produce greater commitment in career choices than those learning experiences that do not reinforce learning-style dispositions.

Taken as a whole, these data present enticing, if not definitive, evidence that career choices tend to follow a path toward accentuation of one’s specialized approach to learning. Learning experiences congruent with learning styles tend to positively influence the choice of future learning and work experiences that reinforce that particular learning style. On the other hand, those students who find a learning environment incongruent with their learning styles tend to move away from that kind of environment in future learning and work choices.

The same pattern of results is found when another aspect of academic performance, student perceptions of how heavy the academic workload is, is examined (see Figure 7.8). Students rated their perception of academic workload on a 1-5 scale, where 1 equaled “very great” and 5 equaled “light.” In mechanical engineering, mathematics, and economics, those students whose learning styles were congruent with their discipline norms felt the workload to be lighter than did those students whose learning styles did not “fit.” (Statistical significance levels in mathematics and economics are p < .05 and p < .10 respectively.) Again, the humanities department showed a trend in the opposite direction.

Mismatches between learning styles and discipline demands are apt to affect a student’s social adaptation to the university. An incongruence between a student’s learning style and the norms of his or her major might well undermine feelings of belonging to the university community and alienate him or her from the power structure (faculty and administration). To test these hypotheses, we used a version of Olsen’s political alienation scale (1969) and McCloskey and Schaar’s anomie scale (1963) that were adapted to apply specifically to the TECH environment (see Kolb, Rubin, and Schein, 1972, for complete details of these scales). These scales measure two uncorrelated aspects of alienation that influence student adaptation. Political alienation results from the failure of authorities, teachers, administrative officials, and the system as a whole to meet the student’s needs. The politically alienated student feels that the authority structure of the university is not legitimate because it is unconcerned about students, because it does not involve them in its decision procedures, because it allows its priorities to be set by vested interests, and because it is incapable of solving the problems it faces. Anomie stems not from dissatisfaction with the formal authority system but from a lack of contact with the norms and values that determine and direct behavior of individuals in the university. These norms and values are communicated most directly through contact with one’s peers. We have found, for example, that feelings of anomie among TECH students are strongly associated with lack of involvement in a personally important group of peers (Kolb et al., 1972). Anomie students feel lonely, isolated, and out of place at TECH. They have difficulty determining what is expected of them and what they believe.

Figures 7.9 and 7.10 show the anomie scores and political alienation scores respectively in the four departments for students whose learning styles are congruent and incongruent with their discipline demands. The results are generally consistent with the prediction that there would be higher anomie and political alienation among those students whose learning style is incongruent with their discipline norms. (None of the political-alienation differences are significant statistically, however. Anomie significance levels for humanities and economics were p < .10 and p < .01, respectively.) One interesting fact in Figure 7.10 is the very high political alienation scores of all students in the humanities department. Humanities, in fact, scored highest on this variable of all the departments in the institute. This further develops the pattern of humanities as a deviant learning environment at TECH.

Further insight into the effect of learning styles on social adaptation can be gained by examining student involvement with an important peer group (Figure 7.11). Students were asked to rate on a 1-to-5 scale (5 = very involved) how involved they were with their most important peer group. As has already been noted, previous research showed high involvement with peers to be associated with low anomie. As Figure 7.11 shows, in all four departments, students with learning styles matching departmental norms tended to be highly involved with their peers. This pattern was most pronounced in the humanities (p < .05) and economics (p < .01) departments. These results suggest that student peer groups may be an important vehicle for the communication of the learning-style requirements of a department, although, as we already know from many studies of formal and informal organizations, peer-group norms may sometimes run counter to the formal organizational requirements. Some evidence for this special role of the peer group can be seen in a comparison of the economics and humanities departments. In both these departments, students whose learning style fits with the discipline demands are very involved with their peers; and both groups of students score very low in anomie, as we would predict. Yet the convergent economics students score very low in political alienation, whereas the divergent humanities students feel extremely politically alienated from the university. Thus, in humanities, student peer-group solidarity among divergers is based on norms of alienation and rebellion from the university, and in economics, the convergent peer-group norms support the goals and procedures of the formal authorities. This may in part account for the fact that the grades of the divergent humanities students are poor relative to those of other humanities students, while the grades of the convergent economics students are far better than those of other students in economics.

The research above illustrates the usefulness of experiential learning theory for describing specialized developmental processes in undergraduate education by describing variations in the ways people learn, and corresponding variations in the learning demands of different academic disciplines. This study of TECH undergraduates shows that, at least in this one institution, the experiential learning theory typology is useful for describing the learning-demand characteristics of different academic departments and for predicting the direction of student career choices; and through examination of the matches and mismatches between student learning styles and departmental learning demands, the typology helps to explain variations in academic performance and adaptation to the university. These results suggest that the experiential learning model may well provide a useful framework for the design and management of learning experiences. As was already noted, the dominant trend in research on the “climate” of learning environments has been to focus on the effect on performance and adaptation of social-emotional variables such as motivation, attitudes, participation, liking for the teacher, and social isolation. Many of these variables have been shown to be important. However, the results of this study suggest that the climate of learning environments might as productively be examined in terms of its effect on the learning process itself and, in particular, on the learning styles of students. Rather than being a cause of successful academic performance, motivation to learn may well be a result of learning climates that match learning styles and thereby produce successful learning experiences. Similarly, the sources of student alienation may lie as much in failures to achieve the university’s central mission—learning—as they do in its social milieu.

As a result, the process of socialization into a profession becomes an intense experience that instills not only knowledge and skills but also a fundamental reorientation of one’s identity. We refer to this orientation as a professional mentality. This mentality is pervasive throughout all areas of the professional’s life; it includes standards and ethics, the appropriate ways to think and behave, the criteria by which one judges value, what is good or bad. Learning style is an important part of professional mentality. It represents the generic learning competencies that facilitate the acquisition of the specific performance skills required for effectiveness in the core professional role. Through processes of selection and socialization, professional schools make every effort to ensure the proper professional mentality in their graduates. This education is a major social control on the quality of the professional service.

A problem arises, however, when we consider the nature of professional careers in a rapidly changing society. As Whitehead observed, “The fixed person for the fixed duties, who in older societies was such a godsend, in the future will be a public danger” (1926, p. 282). Few professionals remain for a lifetime in the core professional role for which they were trained. In engineering, for example, the typical career path requires a transition to management, a job role requiring a different portfolio of competencies and a different learning style from the convergent professional mentality so suited to engineering work. This lifelong career perspective poses a serious dilemma for professional education. Should it continue to emphasize intensive socialization in the specialized role requirements of the profession, or should some of this rigorous specialized training give way to the broader development of learning competencies required for lifelong learning? The choice for broader development may mean less specialized education at a time when the knowledge required for professional competence is increasing. The specialized choice may result in professional deformation—in the intensive overlearning of a specialized professional mentality that actively hinders adaptation to the changing requirements of one’s career.

The dilemma has been central to much of the self-examination, social criticism, and student/alumni evaluation of professional education. Schein, for example, has outlined eight problems of professional education, all of which are related to the dilemma of specialized vs. integrative education:

1. The professions are so specialized that they have become unresponsive to certain classes of social problems that require an interdisciplinary or interprofessional point of view—e.g., the urban problem.

2. Educational programs in professional schools, early career paths, and formal or informal licensing procedures have become so rigid and standardized that many young professionals cannot do the kind of work they wish to do.

3. The norms for entry into the professions have become so rigid that certain classes of applicants, such as older people, women, and career switchers, are in effect discriminated against.

4. The norms of the professions and the growing base of basic and applied knowledge have become so convergent in most professions that it is difficult for innovations to occur in any but the highly specialized content areas at the frontiers of the profession.

5. Professionals have become unresponsive to the needs of many classes of ultimate clients or users of the services, working instead for the organization that employs them.

6. Professional education is almost totally geared to producing autonomous specialists and provides neither training nor experience in how to work as a member of a team, how to collaborate with clients in identifying needs and possible solutions, and how to collaborate with other professionals on complex projects.

7. Professional education provides no training for those graduates who wish to work as members of and become managers of intra- or interprofessional project teams working on complex social problems.

8. Professional education generally underutilizes the applied behavioral sciences, especially in helping professionals to increase their self-insight, their ability to diagnose and manage client relationships and complex social problems, their ability to sort out the ethical and value issues inherent in their professional role, and their ability to continue to learn throughout their careers. [Schein, 1972, p. 59]

3. The research reported in this section is part of a larger research program on professional education and career development (see Kolb and Wolfe et al., 1981). The data reported here were analyzed in collaboration with Ronald Sims.

The science-based professions, and especially engineering, require a highly developed capacity for working with abstract conceptualizations in the utilization of advanced technology for solving real-world problems. The work itself results not so much in further conceptualization (the province of the basic sciences) but rather in action taken to solve practical problems and to develop and construct physical structures, products, and technical processes. Thus, a well-developed competence in active experimentation is equally essential for effective work in the science-based professions. The adaptive competencies—symbolic complexity and behavioral complexity—combine to make up the convergent style that is the forte of the professional engineer.

Career advancement for engineers often involves a promotion to managerial positions that generally require a substantially different mix of competencies. Much less of these managers’ time is devoted to the direct application of scientific knowledge. Although there is a continuing concern for action, the focus is much more on the concrete realities of managing people, planning for various contingencies, setting priorities, and handling administrative tasks. The emergent need, in this transition to management, is for increased competence in handling the complexities and vagaries of concrete experience. The convergent modality must give way to an accommodative style of adaptation, based on competencies in affective complexity (concrete experience) coupled with behavioral complexity (active experimentation). Evidence for this transition in engineering careers is seen in the current job roles of engineering alumni in our study. Of new engineers three years on the job (the 1975 alumni), only 31 percent are managers. This percentage rises steadily over time to the 1955 alumni group, where 76 percent are managers.

Professional work in human-services fields (such as social work) is predicated on highly developed accommodation skills. The emphasis is on dealing with the social and emotional complexities of people in need. The helping process calls for heightened sensitivity to the concrete realities of the human condition, matched with active problem solving. These generic competencies are required for the effective delivery of services to the disadvantaged, the troubled, and the needy.

Career advancement for human-service professionals may also involve taking on managerial responsibilities, but in this case, a change in basic adaptive style may or may not be required. Although a newly appointed director of a social agency generally has many new things to learn, his or her accommodative style is generally appropriate for most of the developmental agenda in this transition. Nonetheless, for many who are promoted from direct-service delivery to administrative or policy-formation assignments, some increase in abstract analytic competencies is called for. One must back away from some of the concrete details of casework with individuals in order to gain a larger perspective. The basic accommodator style begins to require a backup of converger skills or perhaps even assimilator (social-planning) skills. The study of social-work alumni shows a less clear picture of career transition in that field. The percentage of managers in all five alumni groups ranges from 43 to 63 percent, with no clear transitional pattern. These data, along with observations of individual career histories, suggest that careers in social work are primarily of a dual-track nature. People enter the profession with an orientation to management or direct service and tend to stay in that job role more than is the case in engineering.

Thus, engineering and social work seem to have very different career paths. In engineering, there is a definite general progression from direct engineering work to managerial positions over the cohort years, whereas social work appears to have two tracks, administrative and direct service, which begin in graduate school and continue equally in early and late career with less distinct progression from one role to the next. Since professional education must prepare people not only for early career demands but also for the often quite different demands of later career responsibilities, the different structure of career paths in these two fields offers contrasts of great interest for the study of career adaptation.

Equally relevant to our consideration of specialized professional education and its effect on career adaptation is the degree of variation in learning style among social workers and engineers within their professional specialties. In engineering, we were surprised to find a great deal of homogeneity in learning style. There was significantly less variance in LSI scores than in social work. More important, there were no significant differences among alumni cohort years (note that the averages for the five cohort years are all tightly grouped about the engineering mean in Figure 7.12) or among the three major job roles currently held by engineering alumni—manager, technical manager, and “bench” engineer. Insofar as learning style is a part of the professional mentality of engineering, we see here a very consistent professional mentality that varies little even in the face of age, years of work experience, and different job demands. The social-work professionals show much greater variation in learning style. There is wide (although not statistically significant) variation among cohort-year averages, and the two major job roles in social work, administration and direct client service, are significantly differentiated on the active-reflective dimension of the LSI (p < .02), as one might predict from an analysis of demands of these jobs. Administrative job holders are primarily accommodative in their approach to learning, whereas direct-service personnel emphasize the divergent learning style.

Taken together, the results above portray engineering as a field with a highly paradigmatic, coherent professional mentality shaped by the selection and socialization forces of accentuation. A paradigm refers to the body of theory about cause and effect that is subscribed to by all members of the field (Kuhn, 1962). This paradigm serves two important organizing functions: It provides a consistent account of most of the phenomena of interest in a particular area, and it defines those problems that require further research. From these data we would also infer that the social-work profession is a less established and paradigmatic profession than engineering. Schein (1972) identified three trends of maturing professions: (1) They become more convergent in their knowledge base and standards of practice; (2) they become more highly differentiated and specialized; and (3) they become more bureaucratized and rigid with respect to the career alternatives they allow. On all three criteria, the social professions, including social work, are less mature and paradigmatic than are the science-based professions.

One way to assess career adaptation is to measure how well the competencies of the individual professional meet the demands of his or her current job. On the alumni questionnaire, respondents were asked to describe the demands of their current job and their corresponding level of ability to meet these job demands. The technique used was an earlier version of the competency circle described in Chapter 4 (pp. 131–134 and Figure 4.10). Although, as was indicated earlier, these self-ratings of job demand and personal competence would obviously be enhanced by objective independent assessments, the responses of social work and engineering professionals nonetheless reveal interesting patterns of career adaptation. To begin with, differences in job demands were analyzed. The different job roles making up careers in engineering and social work require different portfolios of performance competencies. Engineering jobs require predominately convergent competencies associated with the symbolic and behavioral learning competencies. Managerial jobs in engineering require more affective and behavioral competencies. Direct-service social work requires highly developed affective competencies, and administrative jobs emphasize behavioral competencies more strongly.

To test this hypothesis, one-way analyses of variances were done between the job demands of the different job roles in social work and engineering, and the mean scores were plotted on the competency-circle graphs (Figures 7.13 and 7.14). Figure 7.13 shows great differences in the different job roles comprising engineering careers. Using a Scheffe procedure at .05 level, the significant subsets between job roles are circled. As can be seen in Figure 7.13, professional engineers in the job roles of engineer, technical manager, and manager do perceive their jobs as having different demands in the competencies of dealing with people, being personally involved, and being sensitive to people’s feelings, seeking and exploiting opportunities, making decisions, and setting goals, designing experiments, testing theories, and gathering information. Generally, managerial jobs require greater affective and behavioral competencies, and direct engineering work requires greater symbolic and perceptual competencies.

For social workers, the analysis of variance procedures showed significant differences between job roles on making decisions, seeking and exploiting opportunities, analyzing quantitative data, sensitivity to people’s feelings, and testing theories and ideas (Figure 7.14). Direct-service social workers and administrators perceive their jobs as having different job demands. Direct-service professionals see their jobs as more demanding affectively than do administrators. Administrators perceive their jobs as more demanding behaviorally—for instance, seeking and exploiting opportunities, committing themselves to objectives, and making decisions.

By comparing alumni’s self-ratings of their work abilities with their descriptions of the demands of their current jobs, it is possible to determine the percentage of alumni in different job roles who see themselves as being underqualified in each of the four clusters of performance competencies. These data are shown in Table 7.1.

The data for engineers suggest problems in career adaptation. One-third or more of the technical managers and managers in the engineering alumni sample report that they are underqualified in affective and behavioral competencies. These percentages are greater than the corresponding percentages of bench engineers who are underqualified in affective and behavioral competencies, suggesting that the larger number of underqualified managers in these areas results from a failure to learn how to respond to the increased affective and behavioral demands characteristic of managerial jobs (Figure 7.13). The fact that the number of affectively and behaviorally underqualified managers is greater than symbolically and perceptually underqualified managers suggests that professional education more adequately prepares professionals in symbolic and perceptual competencies than in affective and behavioral competencies (see the following section on learning at school and at work).

In social work, 44 percent of the administrators report that they are underqualified in the behavioral competencies. The increase in the number of behaviorally underqualified administrators over behaviorally underqualified direct-service workers would seem to result from a failure to learn how to respond to the increased behavioral demands of administrative jobs (see Table 7.1). However, a large percentage of administrators see themselves as underqualified in the other three areas of competence, as well as in the behavioral area that appears to be neglected in professional social-work education (Figure 7.16). This suggests that failures of career adaptation in social work are as much a result of a generalized lack of competencies to deal with the professional tasks in the administrative role that are often nearly impossible, as they are a result of professional deformation.

Thus we see differences in how professionals in social work and engineering adapt to the changing demands of their careers. The job roles in engineering and social work require quite different portfolios of performance competencies. Engineering jobs require strong capabilities in symbolic and perceptual areas; direct-service social work emphasizes affective and perceptual competencies. Administrative and managerial jobs across the two professions are very similar, requiring highly developed affective and behavioral competencies.

Both professions seem to have problems of career adaptation, although for different reasons. In social work, it appears that many incumbents of jobs at all levels feel somewhat overwhelmed by the requirements of their jobs. The challenge for professional education would seem to lie in the development of more powerful “social technologies” and educational methods for responding to our country’s increasing social problems in a time of scarce resources.

The problem in engineering may more properly be considered one of professional deformation. The scientific technologies of the various engineering fields with their attendant scientific problem-solving mentality have proven their potency repeatedly. Career adaptation problems in engineering stem more from overspecialization in these learning competencies, often to the point where professionals in the field have difficulty performing in managerial job roles that require greater affective and behavioral competence.

Lower-level accountants had convergent learning styles, and this convergent emphasis was even more pronounced in middle-level accountants, reflecting a highly technical emphasis in the early and middle stages of accounting careers. The senior-level accountants, however, were accommodative in their learning style, reflecting a greater concern with client relations and administration than with technical functions. Marketing professionals at the lower level also were convergent in learning style but became highly concrete at middle-level responsibilities, reflecting a shift from technical to creative concerns. The senior marketing personnel had accommodative learning styles similar to those of senior accountants, probably reflecting the same client and management concerns.

There is a similar transition in the learning orientations of engineers and social workers over the course of their careers. In a selected interview study of the engineering and social-work alumni, Gypen found that:

As engineers move up from the bench to the management positions, they complement their initial strengths in abstract conceptualization and active experimentation with the previously non-dominant orientations of concrete experience and reflective observation. As social workers move from direct service into administrative positions, they move in the opposite direction of the engineers. [Gypen, 1980, p. ii]

Furthermore, Gypen found that these changes in learning style were directly related to the changing demands of one’s current job.

These results give a somewhat more optimistic picture of professional career adaptation than the just-reported data showing that large numbers of professionals describe themselves as underqualified for their current job demands (see Table 7.1). From this perspective, the developmental “glass” appears half full rather than half empty. Some change in the direction of career adaptation does occur after professional education, most likely through learning on the job.

To explore how the social-work and engineering alumni developed their current portfolios of competencies, the alumni questionnaires asked respondents to rate how much their professional education had contributed to the development of each of the performance competencies described in the last section and how much their work experience had contributed to the development of these competencies. Their responses are plotted on the competency circle for engineering alumni in Figure 7.15. The dark shaded area represents contributions of professional education to competencies, and the lined area shows contributions of work experience to the development of competence. The dark line shows the average current job demand for each competence. The figure shows dramatic differences in the competencies acquired in education and work. Engineering education seems to prepare or, in several cases, overprepare people for the demands of their jobs in symbolic and perceptual competencies but makes little contribution to the development of affective and behavioral competencies. These seem to be acquired primarily in the work setting.

Figure 7.16 shows the same analysis for social-work alumni. Here, the shaded area representing the contribution of social-work professional education is larger than in engineering, but it is still biased toward the development of perceptual and symbolic skills. Work experience contributes more to the development of affective and particularly behavioral skills, as in the engineering figure.

Surprisingly, these patterns were not significantly different for different alumni years; alumni only three years out of school (the class of 1975) showed the same pattern as alumni 23 years out (the class of 1955). Engineering alumni in all cohort groups reported that their professional education emphasized the development of symbolic and perceptual skills while neglecting affective and behavioral skills. Social-work alumni in all cohort groups felt that their professional education had developed required competencies in the affective, perceptual, and symbolic areas but had neglected the development of behavioral competencies. Both social-work and engineering alumni consistently felt that they had made up for these deficits as well as supplemented their strengths through experiential learning on the job. In another study, Sims (1981) found that this on-the-job learning is facilitated in those organizations with a strong growth climate characterized by good supervision, advancement potential and autonomy, and a chance to grow and develop, and is inhibited in organizations whose climate is not supportive of learning and development. These results showing the critical role of organization climate have been independently replicated by Margulies and Raia (1967). Lifelong learning is not automatic but must be nurtured in a supportive learning environment, both in school and at work.

Fry has found that each type of environmental orientation can be measured by observing the following variables in the context of a course: the purpose of the major activities, the primary source or use of information, the rules guiding learner behavior, the teacher’s role, and the provision for feedback. These are useful cues, because to a great extent they are controlled by the instructor, faculty, or administration, independently of the learner. Most decisions affecting these aspects of learning environments are made before learner-classroom interactions take place. Using these variables, the following pictures of different types of environments result.

Affectively complex learning environments are ones in which the emphasis is on experiencing what it is actually like to be a professional in the field under study. Learners are engaged in activities that simulate or mirror what they would do as graduates, or they are encouraged to reflect upon an experience to generate these insights and feelings about themselves. The information discussed and generated is more often current/immediate. It often comes from expressions of feelings, values, and opinions by the learner in discussions with peers or the teacher. Such expressions of feelings are encouraged and seen as productive inputs to the learning process. The learner’s activities often vary from any prior schedule as a result of the learner’s needs. The teacher serves as a role model for the field or profession, relating to learners on a personal basis and more often as a colleague than an authority. Feedback is personalized with regard to each individual’s needs and goals, as opposed to comparative. It can come from both peers and the teacher. There is accepted discussion and critique of how the course is proceeding, and thus, specific events within a single class session are often more emergent than prescribed.

Perceptually complex learning environments are ones in which the primary goal is to understand something: to be able to identify relationships between concepts, to be able to define problems for investigation, to be able to collect relevant information, to be able to research a question, and the like. To do this, learners are encouraged to view the topic or subject matter from different perspectives (their own experience, expert opinion, literature) and in different ways (listen, observe, write, discuss, act out, think, smell). If a task is being done or a problem is being solved, the emphasis is more on how it gets done, the process, than on the solution. Success or performance is not measured against rigid criteria. Learners are instead left to conclude, answer, and define criteria of success for themselves. Individual differences in this process are allowed and used as a basis for further understanding. Learners are thus free to explore others’ ideas, opinions, and reactions in order to determine their own perspective. In this process, the teacher serves as a “mirror” or “process facilitator.” He or she is nonevaluative, answers questions with questions, suggests instead of critiquing, and relates current issues to larger ones. The teacher creates a reward system that emphasizes methodology of inquiry versus getting a particular answer. In class sessions, there is planned time spent on looking back at previous steps, events, or decisions in order to guide the learner in future activities.

Symbolically complex learning environments are ones in which the learner is involved in trying to solve a problem for which there is usually a right answer or a best solution. The source of information, topic, or problem being dealt with is abstract, in that it is removed from the present and presented via reading, data, pictures, lecture inputs, and so on. In handling such information, the learner is both guided and constrained by externally imposed rules of inference, such as symbols, computer technology, jargon, theorems, graphical keys, or protocols. There is often a demand on the learner to recall these rules, concepts, or relationships via memory. The teacher is the accepted representative of the body of knowledge—judging and evaluating learner output, interpreting information that cannot be dealt with by the rules of inference, and enforcing methodology and the scientific rigor of the field of study. The teacher is also a timekeeper, taskmaster, and enforcer of schedules of events in order that the learner can become immersed in the analytical exercise necessary to reach a solution and not worry about having to set goals and manage his or her own time. Success is measured against the right or best solution, expert opinion, or otherwise rigid criteria imposed by the teacher or accepted in the field of study. Decisions concerning flow and nature of activities in the class session are essentially made by the teacher and mostly prior to the course.

Behaviorally complex learning environments are those in which the emphasis is upon actively applying knowledge or skills to a practical problem. The problem need not have a right or best answer, but it does have to be something the learner can relate to, value, and feel some intrinsic satisfaction from having solved. This would normally be a “real-life” problem, case, or simulation that the learner could expect to face as a professional. In the attack on the problem, the focus is on doing. Completing the task is essential. Although there may be an externally imposed deadline or periodic checkpoints for which reports or other information are required, most of the learner’s time is his to manage. He is thus concerned with what effect his present behavior will have vis-á-vis the overall task to be done. The next activity he engages in will not occur independent of the one he is presently in. In this way, the learner is always left to make decisions/choices about what to do next or how to proceed. The teacher can be available as a coach or advisor, but primarily in the learner’s request or initiative. Success is measured against criteria associated with the task: how well something worked, feasibility, sellability, client acceptance, cost, testing results, aesthetic quality, and so on.

Learning environments vary in the degrees to which they are oriented to any of the four pure types described above. In a study of a landscape-architecture department (Fry, 1978), ten courses were measured to determine the degree of environmental complexity or the tendency of a course to be oriented in one or more ways. The results indicated that all the courses had degrees of orientation in each area and that it was even possible for a given course to be very affectively and symbolically oriented at the same time. Consistent patterns of environmental orientation showed up in the following combinations: perceptual and symbolic—an “investigative” or “inquiry” climate with the emphasis on inductive theory building and on understanding why things happen (this was most characteristic of lecture and seminar course sessions in this setting); symbolic and behavioral—a “mastery” climate where emphasis was on mastering techniques by practice in problem solving (this was most characteristic of laboratory and recitation sections of courses in this study); behavioral and affective—a “simulative” climate where situations were created to put the learner in the role expected of a graduate in a work setting (this was most characteristic of courses requiring field experience, site visits, and interaction with others outside the classroom in this setting).

The students who scored high in concrete experience as a preferred learning mode indicated that their ability to learn was enhanced by affectively related factors such as personalized feedback, sharing feelings, teachers behaving as friendly helpers, activities oriented toward applying skills to real-life problems, peer feedback, and the need to be self-directed and autonomous. An environmental factor that hindered their learning ability was theoretical reading assignments.

The learners scoring highest in reflective observation reported perceptually related environmental factors as being helpful. These included teachers providing expert interpretations and guiding or limiting discussions, output being judged by external criteria of field or discipline, and lecturing. Reflective learners are not helped by task-oriented situations where information generation was focused on getting some job done.

The learners scoring highest in abstract conceptualization cited symbolically related factors such as case studies, thinking alone, and theory readings as contributing to their ability to learn. They also felt that several elements of affectively and behaviorally oriented environments hindered their ability to learn. These included group exercises and simulations, the need to be self-directed or autonomous, personalized feedback, teachers being models of the profession, sharing personal feelings about subject matter, dealing with “here-and-now” information, and activities oriented toward experiencing being a professional in the field.

Finally, the learners with the strongest active-experimentation tendencies identified several factors as helpful that one would associate with a behaviorally oriented learning environment. These included small-group discussions, projects, peer feedback, homework problems, the teacher behaving as a model of the profession, being left to judge one’s work by oneself, and activities designed to apply skills to practical problems. Things these students reported as hindrances to their ability to learn included lectures, teachers serving as taskmasters, and having their work evaluated as simply right or wrong.

People enter learning situations with an already-developed learning style. Associated with this learning style will be some more or less explicit theory about how people learn, or more specifically, about how they themselves learn best. Learning environments that operate according to a learning theory that is dissimilar to a person’s preferred style of learning are likely to be rejected or resisted by that person. Many students, for example, resist required courses designed to broaden their interests. One way to deal with this problem is for teacher and student to share explicitly their respective theories of learning. From this discussion, the student can gain an insight into why the subject matter is taught as it is and what adjustments he need make in his approach to learning this subject. The discussion can help the teacher to identify the variety of learning styles presented in the class and to modify his/her approach to accommodate these differences. A third benefit from explicit discussion of the learning process as it applies to the specific subject matter at hand is that both teacher and students are stimulated to examine and refine their learning theories. Learning becomes a skill that can be improved and coached. Perhaps the most important implication of the interaction between learning styles and learning environments is that empathy and communication are central to the teaching process. To educate means literally “to draw out.” This requires an ability on the part of the teacher to make contact with the students’ inner resources, attitudes, and ideas and through dialogue to develop and refine their knowledge and skills.

A second practical concern that emerges from our research on learner-environment transactions is the need to individualize instruction. Most traditional classroom teaching methods are too homogeneous as learning environments, appealing to only a single learning style while handicapping those who would prefer to learn another way. Approaches that individualize the learning process to meet the student’s goals, learning style, pace, and life situation will pay off handsomely in increased learning. One key to this individualization is a shift in the teacher role from dispenser of information to coach or manager of the learning process. The widespread use of e-learning innovations is facilitating this role transition by providing alternative modes for delivery of content.

To answer this question, it is necessary to consider the goals of the educational program. In curriculum design, three classes of learning objectives must be considered: content objectives, learning-style objectives, and growth and creativity objectives. Very often in the design of curricula, it is only the content objectives that are explicitly considered: What material should be covered? What concepts should be introduced? What facts does the student need to know? Yet in most academic disciplines, there are also important norms about learning style. Students are expected to adopt certain perspectives on their work. They must learn to think like a mathematician or feel like a poet or make decisions like an executive. Thus, the learning style that is felt to be appropriate for a given area of study must also be considered when educational objectives are set.

To complicate matters further, the third class of objectives must be considered, objectives for growth and creativity. In addition to specialized developmental training, teachers often have objectives concerning the growth and creativity of their students. In making students more “well-rounded,” the aim is to develop the weaknesses in the students’ learning style to stimulate growth in their ability to learn from a variety of learning perspectives. Here, the goal is something more than making students’ learning styles adaptive for their particular career entry job. The aim is to make the student self-renewing and self-directed; to focus on integrative development where the person is highly developed in each of the four learning modes: active, reflective, abstract, and concrete. Here, the student is taught to experience the tension and conflict among these orientations, for it is from the resolution of these tensions that creativity springs.

The dilemma in making choices among these three levels of objectives is illustrated by Plovnick’s study, cited earlier, which concluded that the major emphasis in physics education was on convergent learning, and that physics students who had convergent learning styles were more content with their majors than students whose learning styles did not match the inquiry norms of physics. The dilemma for the physics department is this. To contribute in physics today, one must know many facts, so content learning is important and takes time, time that might be spent developing the convergent skills of divergers. So isn’t it simpler to select (implicitly or explicitly) people who already possess these convergent experimental and theoretical skills? Perhaps, but in the process, what is lost is the creative tension between convergence and divergence. The result of this process may be a program that produces fine technicians but few innovators. Kuhn put the issue this way: “Because the old must be revalued and reordered when assimilating the new, discovery and invention in the sciences are usually intrinsically revolutionary. Therefore they do demand just that flexibility and open-mindedness that characterize and indeed define the divergent” (1962, p. 112). It just may be that one of the reasons that creative contributions in the sciences are made primarily by younger persons (Lehman, 1953) is that the learning styles of older adults have been shaped by their professional training and experience, so that they adapt well to the inquiry norms of their profession but the creative tension is lost.

There is little question that integrative development is important for both personal fulfillment and cultural development. It appears essential for growth and mastery of the period of adult development that Erikson has called the crisis of generativity versus stagnation. The educational issue is how and when to intervene in a way that facilitates this development. The “hows” are not easy. Bok compared the introduction of the new core curriculum to “reorganizing a graveyard.” Specialization in the university is greatly reinforced by faculty reward systems, selection and evaluation criteria, and disciplinary values. The result of these organizational processes is brought into sharp focus when we examine the difficulties and obstacles that attend the establishment of truly interdisciplinary programs of research or teaching, or the struggles for survival and viability that face “deviant” disciplines in an institution where attention, resources, and prestige are focused on the dominant academic culture. Even at a scientific institute like TECH, where the humanities are firmly established with a distinguished faculty, we see intense student alienation, departmental evaluation standards that appear more attuned to the inquiry norms of engineering than to the humanities, and a subtle but powerful imperialism of the dominant scientific culture on research and teaching activities. However useful scientific analyses of the humanities (for example, computer models of Greek myths) may be in their own right, one must ask how well such activities serve to broaden the worldview of the science or engineering specialist.

In the words of Robert Hutchins:

The reverence that natural science has inspired is in large part responsible for the steady narrowing of education that the progress of specialization has caused during the last thirty years. The progress of specialization has meant that the world and ourselves have been progressively taken apart, and at no point have they been put together again. As the progress of the scientific method has discredited the methods of history, philosophy, and art, so the specialism essential to science has discredited a generalized approach to history, philosophy, art and all other subjects, including science itself. This means that comprehension has been discredited, for it cannot be attained by splitting the world into smaller and smaller bits. [Hutchins, 1953, p. 25]

Liam Hudson has examined the effect of relationship between departments and larger organizational processes on the educational process:

At the pragmatic level, for instance, one makes no sense of a certain course in the behavioral sciences, until one sees it as a compromise reached between a number of university departments, each of which has a legitimate interest in its content. The curriculum, simply, is a by-product of a process that is starkly political. And at the conceptual level we find ourselves continually confronting mysterious notions, like that of a “discipline.” To begin to grasp what happens inside universities, we find ourselves compelled to grapple with the boundary rules which surround bodies of knowledge like history, medicine, chemistry or psychology, and to make interpretative sense of their function. We need, too, to come to terms with what one might describe as the politics of knowledge—that territory in which such disciplines take shape, in which schools of thought do battle with one another, and in which certain views of mankind are rendered legitimate, while others are cast beyond the pale. [Hudson, 1976, p. 219]

Thus, it would seem that a central function for the larger university organization is to provide the integrative structures and programs that counterbalance the tendencies toward specialization in student development and academic research. Continuous lifelong learning requires learning how to learn, and this involves appreciation of and competence in diverse approaches to creating, manipulating, and communicating knowledge.

To assume that this integrated appreciation and competence can be achieved solely by distribution requirements or other legislative approaches is highly questionable. As with racial integration, we must closely scrutinize any strategy that requires students to do what we ourselves cannot or will not do. To build integrative programs of teaching and research requires that reward systems, selection and evaluation criteria, and inquiry values be confronted and adjusted to sustain interdisciplinary activity. In addition, some kind of focal point may be required for successful integrative education. Successful interdisciplinary programs I have seen have had a common reference point for integrative activity. At Alverno College, a common reference point is the student’s development as a self-directed learner. The faculty have reconceptualized their liberal arts curriculum to focus on developing abilities, and have created classroom and off-campus learning experiences with sharp attention to the principles of experiential learning theory and practice (Doherty, Mentkowski, and Conrad, 1978). Results from longitudinal studies (Mentkowski and Strait, 1983) confirm that while students as a group enter college with predominant preferences for some learning styles over others, they show equal preferences for the four styles two years later. They shift in their preferences from more concrete to more abstract and from more reflective to more active. These changes have been directly linked to student performance in the curriculum. This suggests that the environmental press of a college dedicated to experiential learning concepts can cause students to be more balanced in their learning style preferences, and when they are ready to enter their specialized professional programs, they have already experienced multiple learning modes. Further, students as a group maintain their more balanced preferences during the last two years of college, even though practically all of them enter a particular career track. Type of major does not contribute to change. Further, alumnae report having to learn in a variety of modes at work, and being able to adapt to continued and unguided learning situations in a variety of settings (Mentkowski and Much, 1982). In the professions, this reference point is often the professional role itself, in which critical functions and tasks emphasize the need for specialized knowledge from various disciplines. In the “pure” academic disciplines, this focus can come through the broad application of a methodology such as systems analysis or through a research problem that requires multiple perspectives. In the humanities, this reference point may be experiential learning, a focus on the “common experiences of life,” reversing a trend toward theorizing and formal specialization that has, in the opinion of W. Jackson Bates, nearly destroyed the field. Bates calls this trend:

. . . the most serious liability that literary study has been gradually creating for itself: the autonomous nature of literature (and the arts) as a separate preserve apart from the common experiences of life. Hazlitt had a point when he said that the arts resembled Antaeus in the fable, who was invincible as long as his feet touched his mother Earth, but who was easily strangled by Hercules once he was lifted from the strength-giving ground. [Bates, 1982, p. 52]

The “when” question may suggest even more fundamental changes. The continuing knowledge explosion and the corresponding rapid rate of change raise serious questions about the current strategy of “front-loading” educational experience in the individual’s life cycle. When, as current labor statistics indicate, the average person will change jobs seven times and careers three times during his or her working life, it makes more sense to distribute educational experiences throughout adult life in order to assist in the preparation for and mastery of these changes. Younger students will need to intersperse more life/work experience between their years of preparation in formal education. Existing programs of field-experience education (internships, work/study, and so on) give testimony to the learning/development payoff of such a mix of academic and practical training (Hursh and Borzak, 1979). The older generation will need the adult work/study version of what used to be called a “liberal arts education” with fresh opportunities to harvest the many fruits of knowledge left behind on their respective climbs to the top. In this model, the university becomes a center for lifelong learning. Integrative learning experiences take on new meaning and vitality when they are directly connected with the integrative challenges of adult life. Discussions of human values and the quality of life are very different with high school graduates than they are with managers of an oil refinery. Quality patient care has one connotation to the idealistic premed student and quite another to the harried medical specialist. Perhaps the richest resources for integrative development lie in the dialogue across age levels that the university for lifelong learning can provide.

—Kierkegaard

My career as an educator, nearly fifty years since beginning as an assistant professor at MIT’s Sloan School of Management in 1965, has in a sense been a journey to become an experiential educator. After an inglorious and painful beginning, lecturing engineering management students about organizational psychology in my first semester, I began searching for a better way to teach and help students learn. As I mentioned in the introduction, the powerful learning I had experienced in Lewin’s experiential T-groups stood in sharp contrast to the bored and disinterested audiences I had faced that first semester. With my colleagues Irv Rubin and Jim McIntyre we began an exciting process of experimenting with experiential learning methods, first using T-groups but soon modifying our approach to use the experiential learning cycle to create more structured experiential exercises that focused on the subject matter topics of organizational psychology. This resulted in the first management textbook based on experiential learning (Kolb, Rubin, and McIntyre, 1971b) that is now in its eighth edition (Osland, Kolb, Rubin, and Turner, 2007). The workbook provides simulations, role plays, and exercises (concrete experiences) that focus on central concepts in organizational behavior, providing a common experiential starting point for participants and faculty to explore the relevance of behavioral concepts for their work. Each chapter is organized around the learning cycle providing the experience, structured reflection and conversation exercises, conceptual material, and personal application assignments.

Our success with this work began what, for me, has been a lifelong fascination with how people learn and how as an educator I can facilitate their learning. Unlike most educators who have a primary subject matter interest other than learning and education, for me how people learn was my primary focus in both my research and practice of teaching. Thus my career has been a lifelong series of action research projects. Three concepts have become central for me in my quest to become an experiential educator:

Creating spaces for learning

Dynamic matching of individual learning style and subject matter requirements by “teaching around the learning cycle”

Focusing on the development of learning skills

In experiential learning theory these dimensions of learning space all come together in the experience of the learner. This concept of learning space builds on Kurt Lewin’s (1951) field theory and his concept of life space. For Lewin, the person and the environment are interdependent variables, a concept Lewin translated into a mathematical formula, B = f(p, e) where behavior is a function of person and environment. As Marrow puts it, “the life space is the total psychological environment which the person experiences subjectively” (1969, p. 35). Life space includes all facts which have existence for the person and excludes those which do not. It embraces needs, goals, unconscious influences, memories, beliefs, events of a political, economic, and social nature, and anything else that might have a direct effect on behavior. To take time as an example, in many organizations today employees are so busy doing their work that they feel that there is no time to learn how to do things better. This feeling is shaped by the objective conditions of a hectic work schedule and also the expectation that time spent reflecting will not be rewarded. Teachers objectively create learning spaces by the information and activities they offer in their course; but this space is also interpreted in the students’ subjective experience through the lens of their learning style. One’s position in a learning space defines their experience and thus defines their “reality.” Lewin stresses the importance for education of defining the learning space in terms of the learner’s experience: “One of the basic characteristics of field theory in psychology, as I see it, is the demand that the field which influences an individual should be described not in ‘objective physicalistic’ terms, but in the way that it exists for that person at that time. . . . A teacher will never succeed in giving proper guidance to a child if he does not learn to understand the psychological world in which that child lives. . . . To substitute for that world of the individual the world of the teacher, of the physicist, or of anybody else is to be, not objective, but wrong” (Cartwright, 1951, p. 62).

The various factors in a given life space are to some degree interdependent, and Lewin strongly maintains that only the dynamic concepts of tension and force can deal with these sets of interdependent facts. This is what led him to define psychological needs as tension systems and their topological representation as vectors to denote motion. He postulates that the particular organization of a person’s life space is determined by a field of forces, both internal needs and external demands that position the individual in a life space composed of different regions. Using map-like representation, the life space could be depicted topologically. Life spaces can vary in a number of dimensions including extension, differentiation, integration, and level of conflict. Lewin introduced a number of concepts for analysis of the life space and a person’s relationship to it that are applicable to the study of learning spaces, including position, region, locomotion, equilibrium of forces, positive and negative valence, barriers in the person and the world, conflict, and goal.

Urie Bronfrenbrenner’s (1977, 1979) work on the ecology of human development has made significant sociological contributions to Lewin’s life space concept. He defines the ecology of learning/development spaces as a topologically nested arrangement of structures each contained within the next. The learner’s immediate setting such as a course or classroom is called the microsystem, while other concurrent settings in the person’s life such as other courses, the dorm, or family are referred to as the mesosystem. The exosystem encompasses the formal and informal social structures that influence the person’s immediate environment, such as institutional policies and procedures and campus culture. Finally, the macrosystem refers to the overarching institutional patterns and values of the wider culture, such as cultural values favoring abstract knowledge over practical knowledge, that influence actors in the person’s immediate microsystem and mesosystem. This theory provides a framework for analysis of the social system factors that influence learners’ experience of their learning spaces.

Another important contribution to the learning space concept is situated learning theory (Lave and Wenger, 1991). Like experiential learning theory, situated learning theory draws on Vygotsky’s (1978) activity theory of social cognition for a conception of social knowledge that conceives of learning as a transaction between the person and the social environment. Situations in situated learning theory, like life space and learning space, are not necessarily physical places but constructs of the person’s experience in the social environment. These situations are embedded in communities of practice that have a history, norms, tools, and traditions of practice. Knowledge resides, not in the individual’s head, but in communities of practice. Learning is thus a process of becoming a member of a community of practice through legitimate peripheral participation (e.g., apprenticeship). Situated learning theory enriches the learning space concept by reminding us that learning spaces extend beyond the teacher and the classroom. They include socialization into a wider community of practice that involves membership, identity formation, transitioning from novice to expert through mentorship, and experience in the activities of the practice, as well as the reproduction and development of the community of practice itself as newcomers replace old-timers.

Finally, as I described earlier (Chapter 5 Update and Reflections) in their theory of knowledge creation, Nonaka and Konno introduce the Japanese concept of ba, a “context that harbors meaning,” which is a shared space that is the foundation for knowledge creation. “Knowledge is embedded in ba, where it is then acquired through one’s own experience or reflections on the experiences of others” (1998, p. 40). Knowledge embedded in ba is tacit and can only be made explicit through sharing of feelings, thoughts, and experiences of persons in the space. For this to happen, the ba space requires that individuals remove barriers between one another in a climate that emphasizes “care, love, trust, and commitment.” Learning spaces similarly require norms of psychological safety, serious purpose, and respect to promote learning.

The experiential learning theory learning space concept emphasizes that learning is not one universal process but a map of learning territories, a frame of reference within which many different ways of learning can flourish and interrelate. It is a holistic framework that orients the many different ways of learning to one another. As Lewin put it, “Actually, the term learning refers to a multitude of different phenomena. The statement ‘Democracy, one has to learn, autocracy is imposed on the person’ refers to one type of learning. If one says that the spastic child has to learn to relax, one is speaking of a different type of learning. Both types probably have very little to do with learning French vocabulary, and this type again has little to do with learning to like spinach. Have we any right to classify learning to high-jump, to get along with alcohol, and to be friendly with people under the same terms, and to expect identical laws to hold for any of these processes?” (Cartwright, 1951, p. 65).

One’s position in the learning space defines their experience and thus defines their “reality.” Since a learning space is in the end what the learner experiences it to be, it is the psychological and social dimensions of learning spaces that have the most influence on learning. From this perspective learning spaces can be viewed as aggregates of the characteristics of the people in them since the people in a particular environment are arguably the dominant feature of it. “Environments are transmitted through people, and the dominant features of a particular environment are partially a function of the individuals who inhabit it” (Strange and Banning, 2001). An individual’s learning style (Chapter 4 Update and Reflections) positions him/her in one of these regions based on their unique equilibrium of forces among acting, reflecting, experiencing, and conceptualizing. A number of studies of learning spaces in higher education have been conducted using the human aggregate approach by showing the percentage of students whose learning style places them in the different learning space regions (Kolb and Kolb, 2005a; Eickmann, Kolb, and Kolb, 2004).

Comparing Learning Spaces in Management and the Arts To illustrate the concept of learning space, the distribution of student learning styles in two institutions of higher education that are engaged in longitudinal institutional development programs to promote learning—the Case Weatherhead School of Management (CWRU) MBA program and the Cleveland Institute of Art (CIA) undergraduate program—are shown in Figures 7.18 and 7.19. The Case Weatherhead institutional development program, reported in Innovation in Professional Education: Steps on a Journey from Teaching to Learning (Boyatzis, Cowen, and Kolb, 1995), focused on curriculum development, student development, and longitudinal outcome assessment (Boyatzis, Stubbs, and Taylor, 2002). MBA student learning style data is from Boyatzis and Mainemelis (2000). The program at the Cleveland Institute of Art is part of a longitudinal study of artistic learning conducted by the Ohio Consortium on Artistic Learning involving a longitudinal study of artistic learning styles, student development workshops, and faculty development seminars Eickmann, Kolb, and Kolb, 2004).

Figures 7.18 and 7.19 show how the learning styles of management and art students are distributed in the learning regions. Art students are concentrated in the experiencing-oriented northern regions of the learning space while management students are concentrated in the thinking southern regions. The figures show that 42.1 percent of art students are in the northern regions while 23.6 percent are in the south, contrasted to 45.7 percent of management students located in the southern regions with 21.2 percent in the north. There are more art students in the eastern reflecting regions than in the western acting regions (35.2 percent to 26.3 percent) and more management students in the western acting regions than the eastern reflecting regions (36.3 percent to 30.4 percent). Among art students the Deciding region is the least populated (3.7 percent) while the least populated region for management students is Imagining (5.1 percent). While 12.5 percent of art students are in the Balancing central region, only 10.2 percent of management students are found there. Boyatzis and Mainemelis found significant correlations between abstract learning styles and grades and GMAT (full-time MBA sample, r = .16; part-time sample, r = .19), indicating a bias toward abstraction in evaluation and selection practices. For BFA graduates there was no relationship between grades and learning style.

The way the educational process is conducted in art schools and management schools reveals some striking differences that give insight into the nature of learning in the different learning regions. Dewey’s distinction between artistic and scientific learning helps us understand the difference between the kinds of learning that occur in art education and in management education:

The rhythm of loss of integration with environment and recovery of union not only persists in man, but becomes conscious with him; its conditions are material out of which he forms purposes. Emotion is the conscious sign of a break, actual or impending. The discord is the occasion that induces reflection. Desire for restoration of the union converts mere emotion into interest in objects as conditions of realization of harmony. With the realization, material of reflection is incorporated into objects as their meaning. Since the artist cares in a peculiar way for the phase of experience in which union is achieved, he does not shun moments of resistance and tension. He rather cultivates them, not for their own sake but because of their potentialities, bringing to living consciousness an experience that is unified and total. In contrast with the person whose purpose is esthetic, the scientific man is interested in problems, in situations wherein tension between the matter of observation and of thought is marked. Of course he cares for their resolution. But he does not rest in it; he passes on to another problem using an attained solution only as a stepping stone on which to set on foot further inquiries.

The difference between the esthetic and the intellectual is thus one of the places where emphasis falls in the constant rhythm that marks the interaction of the live creature with his surroundings. . . . Because of the comparative remoteness of his end, the scientific worker operates with symbols, words and mathematical signs. The artist does his thinking in the very qualitative media he works in, and the terms lie so close to the object that he is producing that they merge directly into it. [Dewey, 1934, pp. 15–16]

A first awareness of differences in the management and arts learning spaces came as we were preparing a learning style workshop for art students. We asked what readings we should give, and the Provost, Paul Eickmann, said, “You know, for art students learning is not text driven.” This stood in dramatic contrast with management education which is almost entirely organized around texts that deliver an authoritative scientific discourse. The scientific basis of the management curriculum was established in 1959 by an influential Carnegie Foundation report that sought to improve the intellectual respectability of management education by grounding it in three scientific disciplines: economics, mathematics, and behavioral science.

The text-driven approach of management education contrasts with the experiential learning process of demonstration-practice-production-critique that is used in most art classes. This process is repeated recursively in art education, while management education is primarily discursive with each topic covered in a linear sequence with little recursive repetition. Management education focuses on telling, while art education emphasizes showing. Management education tends to emphasize theory, while art education emphasizes integration of theory and practice. Art education focuses on the learners’ inside-out expression, while management education spends more time on outside-in impression. Most of the time in management classes is spent conveying information, with relatively little time spent on student performance, most of which occurs on tests and papers. In art classes the majority of the time is spent on student expression of ideas and skills. Art education tends to be individualized, with small classes and individual attention, while management education is organized into large classes with limited individualized attention. An assistant dean at the Columbus College of Art and Design who majored in music as an undergraduate and later got an MBA contrasted the three hours a week he spent in individual tutorial with his mentor with the shock he experienced in entering a tiered MBA classroom of 200 students. Finally, art education tends to be represented by faculty members with diverse learning styles, whereas management education tends to favor specialized faculty members with a primarily abstract learning orientation (see Figure 7.20).

The comparison between the observed educational programs and teaching methods of CIA arts education and Case MBA education seems consistent with respective student LSI distributions in the nine-region learning space, with MBA students primarily in the southern thinking and western acting regions and arts students falling mainly in the northern feeling and eastern reflecting regions. The corresponding discursive, telling educational methods of the MBA program and the recursive, showing techniques of the art school recall Dewey’s description of the scientific worker who “operates with symbols, words and mathematical signs” and the artist who “does his thinking in the very qualitative media he works in.”

Making Space for Engagement in the Learning Cycle The first step in creating an experiential learning space is to make a space for learners that enables deep learning (Border, 2007) by allowing them to fully engage in all four modes of the experiential learning cycle—experiencing, reflecting, thinking, and acting. Learning effectiveness is increased when one can move from one learning mode to the other in the learning cycle entering the different corresponding regions of the learning space. Lewin used the term locomotion to describe this movement through the regions. He spoke of boundaries of the learning regions and how they can act as barriers to entering a region. Fazey and Martin (2002) have argued that learning leads to understanding with greater retention and transfer when an “experiential space of variation” is created through repeated practice from different perspectives and under different conditions. This space of variation can be portrayed as the number of learning regions that a person engages in the learning process. Another popular way of representing this idea is a learning pyramid where learning retention is increased from 20 percent when one learning mode is engaged to 90 percent when all four modes are engaged (Reese 1998; Dale 1969). Although we have seen no studies that have assessed these retention percentages by learning mode empirically, Specht and Sandlin (1991) have shown that retention of accounting concepts after six weeks was 84 percent for students in a course taught using a learning method that followed the experiential learning cycle and only 46 percent in a course taught using the traditional lecture method.

We have seen a polarization between experiencing and thinking in the contrast between the feeling-oriented learning space of CIA arts education and the thinking-oriented learning spaces of the Case MBA program. It seems that educational institutions tend to develop a learning culture that emphasizes the learning mode most related to their educational objectives and devalues the opposite learning mode. Yet, Damasio (1994, 2003), LeDoux (1997), Zull (2002), and others offer convincing research evidence that reason and emotion are inextricably related in their influence on learning and memory. Indeed, it appears that feelings and emotions have primacy in determining whether and what we learn. Negative emotions such as fear and anxiety can block learning, while positive feelings of attraction and interest may be essential for learning. To learn something that one is not interested in is extremely difficult.

Learning is like breathing; it involves a taking in and processing of experience and a putting out or expression of what is learned. As Dewey noted, “. . . nothing takes root in mind when there is no balance between doing and receiving. Some decisive action is needed in order to establish contact with the realities of the world and in order that impressions may be so related to facts that their value is tested and organized” (1934, p. 45). Yet many programs in higher education are much more focused on impressing information on the mind of the learner than on opportunities for the learners to express and test in action what they have learned. Many courses will spend 15 weeks requiring students to take in volumes of information and only a couple of hours expressing and testing their learning, often on a multiple-choice exam. This is in contrast to arts education built on the demonstration-practice-production-critique process where active expression and testing are continuously involved in the learning process. Zull (2002) suggests that action may be the most important part of the learning cycle because it closes the learning cycle by bringing the inside world of reflection and thought into contact with the outside world of experiences created by action.

Respect for Learners and Their Experience We refer to this as the Cheers/Jeers experiential continuum. At one end learners feel that they are members of a learning community who are known and respected by faculty and colleagues and whose experience is taken seriously, a space “where everybody knows your name.” At the other extreme are learning environments where learners feel alienated, alone, unrecognized, and devalued. Learning and growth in the Jeers environment “where nobody knows your name” can be difficult if not impossible. While this principle may seem obvious or even “preachy,” it is problematic for even the finest educational institutions. President Lawrence Summers of Harvard dedicated his 2003 commencement address to the introduction of a comprehensive examination of the undergraduate program, motivated in part by a letter he received from a top science student which contained the statement, “I am in the eighth semester of college and there is not a single science professor here who could identify me by name.” Summers concludes, “The only true measure of a successful educational model is our students’ experience of it” (Summers, 2003, p. 64).

Begin Learning with the Learner’s Experience of the Subject Matter To learn experientially, one must first of all own and value their experience. Students will often say, “But I don’t have any experience,” meaning that they don’t believe that their experience is of any value to the teacher or for learning the subject matter at hand. The new science of learning (Bransford, Brown, and Cocking, 2000) is based on the cognitive constructivist theories of Piaget and Vygotsky that emphasize that people construct new knowledge and understanding from what they already know and believe based on their previous experience. Zull (2002) suggests that this prior knowledge exists in the brain as neuronal networks which cannot be erased by a teacher’s cogent explanation. Instead, the effective teacher builds on exploration of what students already know and believe, on the sense they have made of their previous concrete experiences. Beginning with these or related concrete experiences allows the learner to re-examine and modify their previous sense-making in the light of new ideas.

Creating and Holding a Hospitable Space for Learning To learn requires facing and embracing differences, be they differences between skilled expert performance and one’s novice status, differences between deeply held ideas and beliefs and new ideas, or differences in the life experience and values of others that can lead to understanding them. These differences can be challenging and threatening, requiring a learning space that encourages the expression of differences and the psychological safety to support the learner in facing these challenges (Sanford, 1966). As Robert Kegan says, “. . . people grow best where they continuously experience an ingenious blend of challenge and support” (1994, p. 42). As Kegan implies by his use of the term “ingenious blend,” creating and holding this learning space is not easy. He notes that while educational institutions have been quite successful in challenging students, they have been much less successful in providing support. One reason for this may be that challenges tend to be specific and immediate while support must go beyond an immediate “You can do it” statement. It requires a climate or culture of support that the learner can trust to “hold” them over time. In Conversational Learning (Baker, Jensen, and Kolb, 2002) we draw on the work of Henri Nouwen (1975) and Parker Palmer (1983, 1990, 1998) to describe this challenging and supportive learning space as one that welcomes the stranger in a spirit of hospitality where “students and teachers can enter into a fearless communication with each other and allow their respective life experiences to be their primary and most valuable source of growth and maturation” (Nouwen, 1975, p. 60).

Making Space for Conversational Learning Human beings naturally make meaning from their experiences through conversation, yet genuine conversation in the traditional lecture classroom can be extremely restricted or nonexistent. At the break or end of the class the sometimes painfully silent classroom will suddenly come alive with spontaneous conversation among students. Significant learning can occur in these conversations, although it may not always be the learning the teacher intended. Making space for good conversation as part of the educational process provides the opportunity for reflection on and meaning making about experiences that improves the effectiveness of experiential learning (Keeton, Sheckley, and Griggs, 2002; Bunker 1999). For example, the creation of learning teams as part of a course promotes effective learning when psychologically safe conditions are present (Wyss-Flamm, 2002). Conversational Learning describes the dimensions of spaces that allow for good conversation. Good conversation is more likely to occur in spaces that integrate thinking and feeling, talking and listening, leadership and solidarity, recognition of individuality and relatedness, and discursive and recursive processes. When the conversational space is dominated by one extreme of these dimensions, for example talking without listening, conversational learning is diminished.

Making Space for Development of Expertise With vast knowledge bases that are ever changing and growing in every field, many higher education curricula consist of course after course “covering” a series of topics in a relatively superficial factual way. Yet as the National Research Council in its report on the new science of learning recommends on the basis of research on expert learners, effective learning requires not only factual knowledge, but the organization of these facts and ideas in a conceptual framework and the ability to retrieve knowledge for application and transfer to different contexts (Bransford, Brown, and Cocking, 2000). Such deep learning is facilitated by deliberate, recursive practice on areas that are related to the learner’s goals (Keeton, Sheckley, and Griggs, 2002). The process of learning depicted in the experiential learning cycle describes this recursive spiral of knowledge development. Space needs to be created in curricula for students to pursue such deep experiential learning in order to develop expertise related to their life purpose.

Making Space for Inside-out Learning David Hunt (1987, 1991) describes inside-out learning as a process of beginning with oneself in learning by focusing on one’s experienced knowledge: the implicit theories, metaphors, interests, desires, and goals that guide experience. Making space for inside-out learning by linking educational experiences to the learner’s interests kindles intrinsic motivation and increases learning effectiveness. Under the proper educational conditions, a spark of intrinsic interest can be nurtured into a flame of committed life purpose (Dewey, 1897). Yet learning spaces that emphasize extrinsic reward can drive out intrinsically motivated learning (Kohn, 1993; Deci and Ryan, 1985; Ryan and Deci, 2000). Long ago Dewey described the trend toward emphasis on extrinsic reward in education and the consequences for the teacher who wields the carrot and stick: “Thus in education we have that systematic depreciation of interest which has been noted. . . . Thus we have the spectacle of professional educators decrying appeal to interest while they uphold with great dignity the need of reliance upon examinations, marks, promotions and emotions, prizes and the time honored paraphernalia of rewards and punishments. The effect of this situation in crippling the teacher’s sense of humor has not received the attention which it deserves” (1916, p. 336).

Making Space for Learners to Take Charge of Their Own Learning Many students enter higher education conditioned by their previous educational experiences to be passive recipients of what they are taught. Making space for students to take control of and responsibility for their learning can greatly enhance their ability to learn from experience. Some use the term self-authorship to describe this process of constructing one’s own knowledge versus passively receiving knowledge from others, considering self-authorship to be a major aim of education (Kegan, 1994; King, 2003; Baxter-Magolda, 1999). Others describe this goal as increasing students’ capacity for self-direction (Boyatzis, 1994; Robertson, 1988). The Management Development and Assessment course in the Case MBA program aims to develop student self-direction through assessment and feedback on learning skills and competencies and the development of a learning plan to achieve their career/life goals (Boyatzis, 1994). Bransford, Brown, and Cocking (2000) argue for the development of meta-cognitive skills to promote active learning. By developing their effectiveness as learners (Keeton, Sheckley, and Griggs, 2002), students can be empowered to take responsibility for their own learning by understanding how they learn best and the skills necessary to learn in regions that are uncomfortable for them. Workshops on experiential learning and learning styles can help students to develop meta-cognitive learning skills. At CIA and the Case undergraduate programs, student workshops help students interpret their LSI scores and understand how to use this information to improve their learning effectiveness. John Reese at the University of Denver Law School conducts “Connecting with the Professor” workshops in which students select one of four teaching styles based on the four predominant learning styles that they have difficulty connecting with. The workshop gives multiple examples of remedial actions that the learner may take to correct the misconnection created by differences in teaching/learning styles. Peer group discussions among law students give an opportunity to create new ideas about how to get the most from professors with different learning/teaching styles (Reese, 1998).

Educating is holistic. It is about developing the whole person. Educating the whole person means that the goal of education is not solely cognitive knowledge of the facts, but also includes development of social and emotional maturity. In experiential learning theory terms it is about facilitating integrated development in affective, perceptual, cognitive, and behavioral realms. Rather than acquiring generalized knowledge stripped of any context, learning is situated to the person’s life setting and life path (Lave and Wenger, 1991). The experiential educator needs to resolve some fundamental dilemmas of teaching. Do we focus on the learner’s experience and interests or subject matter requirements? Do we focus on effective performance and action or on a deep understanding of the meaning of ideas? All are required for maximally effective learning. Experiential education is a complex relational process that involves balancing attention to the learner and to the subject matter while also balancing reflection on the deep meaning of ideas with the skill of applying them. It is important to be learner centered, focusing on the prior knowledge of learners to help them construct new understanding and tapping into their unique interests to increase motivation to learn. It is also important to be subject centered, staying on top of one’s field and organizing subject matter in a form that can be communicated effectively. Similarly, it is necessary to explore the deeper meaning of ideas and develop learners’ ability to critically evaluate the assumptions that underlie them, while also focusing on performance, the effective application of the ideas.

The simple prescription, made by many learning style approaches to match teaching style to the learning style of learners, is not sufficient to deal with this complexity (Pashler et al., 2008; Willingham, 2005, 2009). Unlike the experiential learning theory process-oriented learning style approach, these style measures see learning styles as fixed traits or personality characteristics. Scott, citing Dweck (2000), argues that this is an entity approach to ability that promotes stereotyping and labeling rather than a process approach that emphasizes developmental potential and contextual adaptation. Also, surprisingly, none are based on a comprehensive theory of learning. The dimensions of individuality that they assess are hypothesized to influence learning, but how the dimension connected to the learning process is not made explicit. An individual may prefer to work alone or in a group, but how is this preference related to learning?

Willingham provides an example of the problems that a trait-based learning style measure that is not related to a theory of learning faces with the matching teaching and learning style approach. The VAK measures individual differences in preferences for visual, auditory, and kinesthetic sense modalities. While there are reliable individual differences in preference/ability for these modalities, “For the vast majority of education, vision and audition are usually just vehicles that carry the important information teachers want students to learn” (2005, p. 3). But the process of learning usually involves storing information in memory in terms of meaning, independent of any modality. His review of studies that match sensory modality with instruction concludes: “We can say that the possible effects of matching instructional modality to a student’s modality strength have been extensively studied and have yielded no positive evidence” (2005, p. 3).

The major implication of experiential learning theory for education is to design educational programs in a way that teaches around the learning cycle so that learners can use and develop all learning styles in a way that completes the learning cycle for them and promotes deep learning. Earlier (Chapter 1 Update and Reflections, pp. 26–27) I described Vygotsky’s law of internalization and the zone of proximal development where the child’s novel capacities begin in the interpersonal realm and are gradually transferred into the intrapersonal realm (Vygotsky, 1978). The key technique for accomplishing this transition is called “scaffolding.” In scaffolding the educator tailors the learning process to the individual needs and developmental level of the learner. Scaffolding provides the structure and support necessary to progressively build knowledge. The model of teaching around the cycle described below provides a framework for this scaffolding process. This approach requires competence in relating to learners in complex ways—ways that help them feel, perceive, think, and behave differently. These ways of relating require the educator to play multiple roles in relationship to the learners and the object of the learning endeavor.

In our interviews and observations of experienced, successful educators we find that they tend to “teach around the learning cycle” in this manner. They organize their educational activities in such a manner that they address all four learning modes—experiencing, reflecting, thinking, and acting. As they do this, they lead learners around the cycle, shifting the role they play depending on which stage of the cycle they are addressing. In effect the role they adopt helps to create a learning space designed to facilitate the transition from one learning style to another. Often they do this in a recursive fashion, repeating the cycle many times in a learning program. In effect the cycle becomes a spiral with each passage through the cycle deepening and extending learners’ understanding of the subject. When a concrete experience is enriched by reflection, given meaning by thinking, and transformed by action, the new experience created becomes richer, broader, and deeper. Further iterations of the cycle continue the exploration and transfer to experiences in other contexts.

The New Zealand Ministry of Education (2004) has used this spiraling learning process as the framework for the design of middle school curricula. Figure 7.21 describes how teachers use the learning spiral to promote higher level learning and to transfer knowledge to other contexts.

An educator role is a patterned set of behaviors that emerge in response to the learning environment, including students and the learning task demands. Each educator role engages students to learn in a unique manner, using one mode of grasping experience and one mode of transforming experience. In the facilitator role, educators draw on the modes of concrete experience and reflective observation to help learners get in touch with their own experience and reflect on it. Subject matter experts, using the modes of reflective observation and abstract conceptualization, help learners organize and connect their reflection to the knowledge base of the subject matter. They may provide models or theories for learners to use in subsequent analysis. The standard setting and evaluating role uses abstract conceptualization and active experimentation to help students apply knowledge toward performance goals. In this role, educators closely monitor the quality of student performance toward the standards they set, and provide consistent feedback. Finally, those in the coaching role draw on concrete experience and active experimentation to help learners take action on personally meaningful goals. These roles can also be organized by their relative focus on the student versus the subject and action versus knowledge as illustrated in Figure 7.22.

The Educator Role Profile (ERP) describes four role positions—Facilitator, Expert, Evaluator, and Coach. Educators play these roles as they help learners maximize learning by moving through the four stages of the experiential learning cycle.

The Facilitator Role. When facilitating, educators help learners get in touch with their personal experience and reflect on it. They adopt a warm affirming style to draw out learners’ interests, intrinsic motivation, and self-knowledge. They often do this by facilitating conversation in small groups. They create personal relationships with learners.

The Expert Role. In their role as subject expert, educators help learners organize and connect their reflections to the knowledge base of the subject matter. They adopt an authoritative, reflective style. They often teach by example, modeling and encouraging critical thinking as they systematically organize and analyze the subject matter knowledge. This knowledge is often communicated through lectures and texts.

The Evaluator Role. As a standard setter and evaluator, educators help learners master the application of knowledge and skill in order to meet performance requirements. They adopt an objective, results-oriented style as they set the knowledge requirements needed for quality performance. They create performance activities for learners to evaluate their learning.

The Coaching Role. In the coaching role, educators help learners apply knowledge to achieve their goals. They adopt a collaborative, encouraging style, often working one-on-one with individuals to help them learn from experiences in their life context. They assist in the creation of personal development plans and provide ways of getting feedback on performance.

While Figure 7.23 depicts an idealized sequential progression through the educator roles and learning styles, in most cases a curriculum design will be based on a sequence of activities and instructional techniques that fits the subject matter and learning objectives that may or may not fit such an orderly progression. In considering a design it is useful to consider for each segment the teaching role to adopt, the learning style that you want to engage, and the choice of instructional technique best suited to the learning style and role.

Lipshitz (1983) underscores the complexity of roles for an experiential educator who needs to have a firm grasp of the relevant conceptual material and develop sensitivity and skill in managing learners’ emotional reactions to the learning process. Learners may also react to the shifting role of the educator from that of a knowledge purveyor to one that creates the learning environment and facilitates the holistic learning process. McGoldrick, Battle, and Gallagher (2000) indicate that the less control instructors exert on the students’ experiences the more effective the learning outcome will be. However, instructors may run the risk of losing control over course structure and failing to keep the learning activities bounded within a specific time frame. Most of the risks associated with the experiential method, contend the authors, can be mitigated through careful planning, unambiguous course structure, establishing of clear expectations, and firm deadlines for each class activity.

A learning skill is a combination of ability, knowledge, and experience that enables someone to do something well in a specific context. Learning skills can be intentionally developed by practice. Like the adaptive competency circle, learning skills are arrayed around the learning cycle based on their relationship to learning style. The LSP contains 72 learning skills in 12 scales of a “learning cycle clock.” The four learning modes encompass three scales each. CE includes interpersonal skills of leadership (11), relationship (12), and help (1). RO includes information skills of sense making (2), information gathering (3), and information analysis (4). AC includes analytical skills of theory building (5), quantitative analysis (6), and technology (7). AE includes behavioral skills of goal setting (8), action (9), and initiative (10). The LSP was validated by a confirmative factor analysis of the scales that determined their fit in the four learning mode categories (Kaskowitz, 1995), examination of scale intercorrelations, relationship to the LSI, and correspondence to supervisor ratings (Boyatzis and Kolb, 1995). The LSP items are rated on a 7-point scale and can be adapted to self ratings of one’s skill and job demands and also in a “360” format where the skill and job demands of an individual are rated by supervisors and/or peers.

The LSP has been used extensively for individual personal development and career planning. Figure 7.24 shows the learning skill profiles for two individuals. The woman on the left is a senior human resource development manager who is successful and loves her job. Note the correspondence between her job demands (the solid line) and learning skills (the dashed line) and the high levels of both. The profile on the right is an undergraduate man who is mapping the demands of his prospective major in biomedical engineering with his learning skills. Note the relative match between analytic demands and skills in the southern regions and the mismatch between high interpersonal skills and career demands in the north. This may be why he was questioning whether this major was the right choice. Sometimes when individuals take the LSP, they discover that their job is not using many of their highly developed skills. They are bored and restless, and that is a sign to search for new opportunities. Understanding your learning style and skills and the corresponding demands of different jobs and careers as described in the career chart can help you find that challenging match and a job you love.

The LSP has had a number of educational applications. Baker, Pesut, Mcdaniel, and Fisher (2007) evaluated a new problem-based learning (PBL) curriculum in graduate nursing education and a new PBL MBA program comparing before-and-after LSP results. Kretovics (1999) studied the graduating class of an MBA program in comparison to entering students and a control group finding changes in LSP scores that demonstrated the “added value” of the program. In Innovation in Professional Education (Boyatzis, Cowan, and Kolb, 1995) the LSP was used extensively to assess changes from entering to graduating MBA students. The instrument was also used to interview faculty about the learning skill objectives of their courses. Aggregate faculty scores on skill emphasis were related to student outcomes as measured by changes from entering to graduating students.

In health care, Rainey, Heckelman, Galazka, and Kolb (1993) used the LSP as a team-building and faculty development tool in a family medicine department, while Smith (1990) used the LSP to assess the critical competencies of physician executives. The instrument has been used as a framework for describing managerial (Camuffo and Gerli, 2004) and leadership skills (Kolb and Rainey, 2014). Dreyfus (1989) used the LSP to differentiate the learning skills of high-performing and typical managers.

Yamazaki (2010e) and Yamazaki and Kayes (2010) have used the LSP extensively to study cross-cultural differences and ex-patriate cross-cultural adaptation (Yamazaki, 2010a, 2010b, and 2010c; Yamazaki and Kayes, 2004, 2007).