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Exploring Cognitive Diversity And The Level Style Distinction From A Problem Solving Perspective

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Conference

2009 Annual Conference & Exposition

Location

Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009

ISSN

2153-5965

Conference Session

Student Learning

Tagged Division

Educational Research and Methods

Page Count

21

Page Numbers

14.613.1 - 14.613.21

Permanent URL

https://peer.asee.org/4833

Download Count

234

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Paper Authors

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Kathryn Jablokow Pennsylvania State University-Great Valley

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Dr. Kathryn W. Jablokow is an Associate Professor of Mechanical Engineering and STS (Science, Technology, and Society) in the School of Graduate Professional Studies at the Pennsylvania State University. A graduate of The Ohio State University (Ph.D., Electrical Engineering, 1989), Dr. Jablokow's teaching and research interests include problem solving, invention, and creativity in science and engineering, as well as robotics and computational dynamics. In addition to her membership in ASEE, she is a Senior Member of IEEE and a Fellow of ASME; she also serves as an ABET Program Evaluator and as Vice-Chair of ASME’s Technology & Society Division. Dr. Jablokow has developed and teaches a 4-course graduate-level module focused on problem solving leadership and is currently investigating the impact of cognitive style in invention and design.

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Pamela Vercellone-Smith Penn State Great Valley

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Dr. Pam Vercellone-Smith is an Instructor as well as a Research Associate in the School of Graduate Professional Studies at the Pennsylvania State University. Dr. Vercellone-Smith earned her Ph.D. in Microbiology from the University of Delaware, an M.S. in Software Engineering from Penn State Great Valley, an M.S. in Microbiology from Virginia Tech, and a B.S. in Biology from Virginia Tech. Dr. Vercellone-Smith’s interdisciplinary research interests include problem solving in science and engineering, software complexity and bioinformatics. Dr. Vercellone-Smith is a member of the American Society for Microbiology and the Association for Computing Machinery.

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Sally Sue Richmond Penn State Great Valley

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Mrs. Sally Sue Richmond is a Lecturer in IS (Information Science) in the School of Graduate Professional Studies at the Pennsylvania State University. A graduate of The Pennsylvania State University (MSIS, 1997), Mrs. Richmond’s teaching and research interests are in Human-computer Interaction (HCI), telecommunications and networking, the Software Development Life Cycle (SDLC), and IT Architecture. She is a Member of IEEE and ACM. Mrs. Richmond makes extensive use of technology to supplement face-to-face instruction. She is currently a doctoral student at Nova Southeastern University, where she is researching the impact of cognitive style on the use of software features when exploring large information spaces.

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Exploring Cognitive Diversity and the Level-Style Distinction from a Problem Solving Perspective

Key Words: Adaption-Innovation theory, cognitive style, diversity, problem solving

Introduction

The importance of understanding the cognitive diversity of our students (and ourselves, as their instructors) is clear: the more we know about the way they (and we) think, the better we can develop our courses and tailor our teaching to ensure the best and most effective learning experiences possible for the largest number of students. As Felder and Brent9 note: “Students have different levels of motivation, different attitudes about teaching and learning, and different responses to classroom environments and instructional practices. The more thoroughly instructors understand the differences, the better chance they have of meeting the diverse learning needs of all of their students.” Among the models of cognitive diversity available to us, Kirton’s Adaption-Innovation (A-I) theory19 provides special insight with respect to our students’ efforts in solving problems. In particular, A-I theory identifies four principal variables that can be used to explain many of the variations we see among our students as they solve problems, namely: cognitive level (capacity/resource), cognitive style (preferred approach), motive (driving force), and opportunity (including one’s perception of it). In this paper, we will focus on cognitive style, its diversity among our students, and the importance of distinguishing between style and level in engineering education.

In general, cognitive level is a unipolar construct that relates to one’s mental capacity, both potential (e.g., intelligence, aptitude, talent) and manifest (e.g., extant knowledge, skill, experience); the latter can be measured in terms of both type (i.e., domain – discipline, area of study) and degree (i.e., amount – novice, expert) 19,23. Cognitive style is defined as a “strategic, stable characteristic – the preferred way in which people respond to and seek to bring about change” (including the solution of problems) 19. As such, cognitive style is a bipolar construct that is independent from level; it also has multiple dimensions, including Adaption-Innovation (A-I) and Introversion-Extraversion, among others. Here, we will focus on A-I cognitive style and its assessment using KAI® (the Kirton Adaption-Innovation inventory)18, which has been rigorously validated and used in a wide variety of contexts, including engineering, business, education, medicine, and the military.

As measured by KAI, an individual’s cognitive style is related to the amount of structure he or she prefers when solving problems, with the more Adaptive preferring more structure (with more of it consensually agreed), and the more Innovative preferring less structure (with less concern about consensus). These individual differences are immediately relevant for individual students as they strive to solve problems with varying degrees of structure (e.g., open-ended vs. closed- ended, tightly constrained vs. loosely constrained, etc.) and to meet the expectations of their instructors, whose own styles will influence the solutions they value most. For example, a more Adaptive instructor is likely to expect more careful attention to detail in a completed assignment, while a more Innovative instructor will tend to place higher value on “out-of-the-box” ideas.

Jablokow, K., & Vercellone-Smith, P., & Richmond, S. S. (2009, June), Exploring Cognitive Diversity And The Level Style Distinction From A Problem Solving Perspective Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. https://peer.asee.org/4833

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