Pittsburgh, Pennsylvania
June 22, 2008
June 22, 2008
June 25, 2008
2153-5965
Mechanical Engineering
14
13.175.1 - 13.175.14
10.18260/1-2--3440
https://peer.asee.org/3440
668
Roman Taraban is Professor and Associate Chair in the Department of Psychology at Texas Tech University, Assessment Coordinator for the Texas Tech University Howard Hughes Medical Institute (TTU/HHMI) Biological Sciences Education Program, Member of the Texas Tech Teaching Academy Executive Council, past President of the Society for Computers in Psychology (SCiP), and Associate Editor for the Journal of Educational Psychology. He received his Ph.D. in cognitive psychology from Carnegie Mellon University. His interests are in how undergraduate students learn, and especially, how they draw meaningful connections in traditional college content materials (e.g., textbooks, lectures, multi-media). Address: Department of Psychology, Mail Stop 2051, Texas Tech University, Lubbock, TX, 79409; telephone: 806-742-3711 ext. 247; fax: 806-742-0818; email: roman.taraban@ttu.edu.
Edward E. Anderson is Professor in the Department of Mechanical Engineering at Texas Tech University where he currently serves as the Ray Butler Distinguished Educator. Since returning to the faculty after several different administrative assignments, including Departmental Chairman, Assistant Dean, and Director of the TTU Teaching, Learning and Technology Center, he has focused upon engineering student learning research with an eye upon how to use these findings to improve traditional and computer-based learning.
Curtis Craig is a graduate student in experimental psychology at Texas Tech University, with a disciplinary emphasis on applied cognition.
Jacob Fleming is a graduate student in the experimental psychology program at Texas Tech University.
Alli DeFinis is a graduate student in the counseling psychology program at Texas Tech University.
Ashlee Brown is a graduate student in counseling psychology at Texas Tech University, with an interest in philosophy.
An Assessment of Problem Solving Processes in Undergraduate Statics
Abstract
Four well-articulated models that offer structured approaches to problem solving were identified in the engineering research literature. These models provided a conceptual base for the study reported here. Four undergraduates enrolled in statics and two engineering faculty members provided think-aloud data as they solved two statics problems. The data were used to develop a coding system for characterizing engineering students’ behavioral and cognitive processes. These codes were used to analyze students’ problem solving procedures in a detailed manner, particularly differences between good- and not-so-good problem solvers. The analyses provide a picture of how students and faculty solve problems at a cognitive level, and indicate that published problem-solving models are incomplete in describing actual problem-solving processes.
Wankat and Oreovicz1 asserted that “engineering education focuses heavily on problem solving.” This assertion would find significant agreement among engineering educators. The high proportion of time spent solving textbook problems outside of class by engineering undergraduates has been documented in the engineering research literature2. The central place of problem solving in engineering has led some scholars to inquire about the nature of effective problem solving, asking about the processes that underlie good problem solving procedures. Engineering educators have also developed didactic models meant to guide classroom practices. The research presented here is based on four well-articulated models that offer structured approaches to problem solving. The models have been developed as a response to students’ use of a “hodgepodge of tricks” to solve statics, dynamics, and thermodynamics problems, and they were regarded by their authors as useful to students in developing good problem solving skills. Therefore the models were considered appropriate for an empirical study of problem solving by undergraduates.
The goals of this study were to develop a descriptive language for characterizing engineering students’ behavioral and cognitive processes related to problem solving. This descriptive language was developed as a coding system that was used to analyze students’ problem solving procedures in a detailed manner. These codes were used to evaluate the extent to which the four underlying models captured students’ problem solving processes. The codes were also used to characterize processing differences between good and not-so-good problem solvers. In summary, the goals were: • To develop a coding system for describing problem solving processes • To test the adequacy of four models for describing problem solving processes • To use the coding system to examine differences between good and not-so-good problem solvers. The central method for addressing the questions in this study was the collection and analysis of verbal protocol (“think-aloud”) data. Verbal protocols are open-ended think-
Taraban, R., & Anderson, E., & Craig, C., & Fleming, J., & DeFinis, A., & Brown, A. G. (2008, June), An Assessment Of Problem Solving Processes In Undergraduate Statics Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--3440
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