Problem Solving In Statics Involves Mental Search
- Conference
- Location
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Austin, Texas
- Publication Date
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June 14, 2009
- Start Date
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June 14, 2009
- End Date
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June 17, 2009
- ISSN
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2153-5965
- Conference Session
- Tagged Division
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Mechanical Engineering
- Page Count
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10
- Page Numbers
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14.982.1 - 14.982.10
- DOI
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10.18260/1-2--5001
- Permanent URL
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https://peer.asee.org/5001
- Download Count
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511
Paper Authors
Roman Taraban Texas Tech
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 Anderson Texas Tech
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 Texas Tech
Curtis Craig is a graduate student in experimental psychology at Texas Tech University, with a disciplinary emphasis on human factors psychology.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Problem-Solving In Statics Involves Mental Search
Introduction
Theory of Human Problem Solving. In their seminal work on human problem-solving, Newell and Simon1 described the process of solving a problem as consisting of finding a path that leads from an initial state to the goal state in a problem space. A problem space consists of discrete problem states, which are simply explicit configurations of the problem elements. The initial state consists of a description of the problem elements at the outset of problem solving. Through the application of problem solving operators, a person is able to transform the current problem state into the next problem state. A classic example of the elements of this theory is the Tower of Hanoi puzzle, as shown in Figure 1, consisting of three moveable disks and three pegs. Beginning with an initial state and a given goal state, the initial state can be changed to the next state by applying the operator “Move disk C from peg 1 to peg 3,” for example. Constraints on the operators include moving only one disk at a time, never placing a larger disk on a smaller disk, and never taking a disk out of play. The Newell and Simon theory has been applied to visual puzzles like the Tower of Hanoi, but applies readily to other types of representations and problems, including scheduling problems, decision-making, game-playing, language, and mathematics2.
Figure 1. Tower of Hanoi Example
Initial State Goal State
C C B B A A
The size of the problem space for a typical game of chess3 is 10117. In spite of the immensity of the problem space for chess, even beginning players can play a respectable game. Humans, faced with the task of chess or other problems, rely on heuristic search of problem spaces. Heuristic methods often do not require a great deal of specific knowledge about the problem. Because they do not require specific knowledge, they are widely applicable and therefore very useful. The description of problem solving as heuristic search in a problem space has proven to be quite powerful in understanding humans’ knack for solving simple and complex problems, and those that are well-defined and those that are ill-structured4. The operational parameters of this theory are clearly quite broad, applying as much to the astounding performance of Gary Kasparov against the massively parallel computer Deep Blue5, at one extreme, and the performance of a
Taraban, R., & Anderson, E., & Craig, C. (2009, June), Problem Solving In Statics Involves Mental Search Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--5001
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