Investigating Learning And Technology Using The Mbti And Kolb's Lsi

This presentation will share the findings of a three-year study at the University of Cincinnati on the effects of using technology on student learning in two basic engineering courses. We will compare our results to existing data on engineering students and practicing engineers, as well as discuss whether technology affected performance among various groups. Findings may clarify whether the learning by students of a particular learning style and personality preference is enhanced by specific technologies and may indicate ways in which teaching and learning can be improved. Although many engineering colleges are beginning to use the Internet and Worldwide Web to enhance routine classroom instruction, very little has been done to match the use of new technologies to the learning styles of students. In this study we want to see if it is possible to match the use of new teaching technologies and student performance with learning styles. We use "learning style" to describe the phenomenon of how students learn. In this study, three different technologies were used: Streaming Video, Web-based and Interactive Video which was evaluated separately at the originating and receiving sites, resulting in four separate categories. A standard lecture section was used as the control group. Faculty worked together as a team to select common texts, develop common syllabi and block final exams resulting in standardized materials and grading. To identify learning style, the project team selected two well-known instruments: the Myers-Briggs Type Indicator (MBTI) and the Kolb Learning Style Inventory (LSI). Both inventories were administered to students in all sections of Mechanics I and later in all sections of Basic Strength of Materials at the University of Cincinnati College of Engineering starting in the fall of 1999. In addition, two sections of Mechanics I at Wright State University also participated in the first year of the study. Results to be reported are based on data from a sample of more than 400 students. Preliminary indications are that the use of streaming video and interactive video may improve student performance among personality preferences and learning styles that are less common among students in most engineering classes. P ge 764.1 Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition. Copyright © 2002, American Society for Engineering Education Introduction Society has long searched for ways to improve learning and to increase teaching efficiency. What learning style and technology have in common is that both seem to promise these results. The present study investigates whether a connection can be demonstrated among student learning style or personality type, the use of educational technology, and improved student learning. The study was conducted by a team from the University of Cincinnati under a three-year grant from the General Electric Fund. In addition to engineering students enrolled at the University of Cincinnati, engineering students at Wright State University also participated in the first year of the study. Two widely accepted instruments, the Myers-Briggs Type Indicator (MBTI) and Kolb's Learning Style Inventory (LSI), were selected to provide information on how students learn. The MBTI has been used extensively in studies with engineering students, and we hope to build on that current knowledge base. We plan to use the data gathered to determine implications for teaching and using technology to enhance learning. Description of Program All classes received in-person instruction which varied depending on the specific instructional technology used in that class. The traditional class and the interactive video classes were standard lectures. Students in the web-based class and the streaming video class were required to preview the Mechanics I course material prior to the class. The instructor’s role changed from the traditional lecturer to that of mentor; he reviewed difficult concepts, answered questions, worked problems and gave practical examples. A statistical analysis was used to assess student learning based on MBTI types and LSI in the control class and each of the three technology classes. We examined how various personality types and learning styles perform within a specific class, how various personality types and learning styles perform across all four instructional formats; and how student interest in the class or instructional technology affects his/her grades. The students' learning styles and personality types were measured and compared to student performance in the Mechanics I and Basic Strength of Materials classes taught using four different instructional technologies: interactive video, originating and receiving, a web-based class and a streaming video class. A standard lecture class was used as the control class. There were approximately 400 students enrolled in the five classes. Data acquired during this spring will provide additional samples that will significantly increase confidence in the results. Significant differences were found between the web-based and streaming video classes as well as the web-based and traditional classes. Analysis revealed that differences in thinking types (ST/NT on the MBTI scale) played an important role in grade performance in classes with different instructional technologies. We were unable to detect the role that LSI learning styles may have on student performance in a specific P ge 764.2 Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition. Copyright © 2002, American Society for Engineering Education technology class. LSI results conformed to the national norms for engineering students with the students with convergent learning styles performing slightly better than students with other learning styles. 1 Brief Descriptions of Psychological Type The Myers Briggs Type Indicator (MBTI) uses C.G. Jung's theory of psychological type to indicate how an individual's behavior is orderly and consistent based on differences in the way individuals prefer to use their perception and judgment. Perception involves the ways of becoming aware of people, things, events and ideas. Judgment involves the ways of coming to conclusions about what has been perceived. Differences in perceptions and conclusions result in differing reactions, interests, values and motivations and skills. The MBTI has four separate indices that reflect the four preferences used in perception and judgment. These preferences reflect not only what people attend to in a given situation but also how they draw conclusions about their perceptions. The main objective of the MBTI is to identify four basic preferences. These preferences or indices, EI, SN, TF, and JP are designed to point in one direction or the other. They are not designed to measure traits or behaviors. The intent is to reflect a habitual choice between rival alternatives, analogous to right-handedness or left-handedness. Just as everyone expects to use both hands but generally prefers one to the other, every person tends to use both poles of the preferences but responds first with the preferred functions or attitudes. It is very important to understand that one preference is not qualitatively better than or inferior to another. They are simply different. By identifying individual differences in basic functions and attitudes, the MBTI can be used in a wide range of human activities. In education the MBTI is useful in: ∑ Developing different teaching methods to meet the needs of different types ∑ Understanding type differences in motivation for learning ∑ Analyzing curricula, methods, media and materials to meet the needs of different types. Based on a review of literature, we found that students in engineering and practicing engineers have personality types that are similar across programs and businesses. The distribution of personality types among engineers is somewhat different from that of the general population (see Table I). "Engineering attracts significantly more thinking than feeling types. Thinking types in theory should be motivated to work with concepts and materials that work on logic and cause-effect. " according to McCaulley, Macdaid and Walsh. 2 They continue, "Engineering students and practicing engineers have more judging types than perceptive types. We predicted that J students who value system and order might have an easier time in engineering than P students who value a more adaptive or spontaneous approach. " P ge 764.3 Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition. Copyright © 2002, American Society for Engineering Education Felder 3 stated that "engineering professors usually orient their courses toward introverts (by presenting lectures and requiring individual assignments rather than emphasizing active class involvement and cooperative learning), intuitors (by focusing on engineering science rather than design and operations), thinkers (by stressing abstract analysis and neglecting interpersonal considerations), and judgers (by concentrating on following the syllabus and meeting assignment deadlines rather than on exploring ideas and solving problems creatively). " These studies are of particular importance to our study at the University of Cincinnati. While the percentages in feeling and thinking types among UC students were consistent with the findings of the ASEE Consortium and MBTI studies reported in the Atlas of Type Tables and with new studies such as those reported by Thomas, et. al. , the reported percentages in judging and perceiving types were not. This could be an important finding for further study concerning retention of more students who are perceptive in type. If the use of technologies enhances learning for perceptive types, it could be an important factor in retaining these students. Table I. Comparison of Type for Engineers among Samples from Va