June 22, 2003
June 22, 2003
June 25, 2003
8.286.1 - 8.286.10
Case-Based Reasoning for Engineering Statistics
George Runger, Sarah Brem, Norma Hubele, Toniann Rotante, Kathryn Kennedy Arizona State University
Abstract In this paper, we report on the formulation and early results of research supported by the National Science Foundation’s Experimentation and Laboratory-Oriented Studies Division (DELOS). Using findings from cognitive science, we discuss the design of an intelligent tutoring system (ITS) that utilizes case-based reasoning (CBR) to scaffold undergraduate engineering students in their learning of introductory probability and statistics. Such a system will: • Assist students in extracting the underlying common structure from engineering statistics problems that illustrate the full range of engineering disciplines. • Allow the students to generate, customize, and change a virtually infinite collection of exercises that can be solved with the assistance of the ITS. The students can explore the effect of changes to solutions. • Help students formulate and solve "practical" and "open-ended" problems, a skill stressed by the ABET Engineering Criteria.
Introduction Several trends have converged to make this an important project at this time: • Psychological and computational advances in CBR that allow us to use processes that model human thought, rather than those that are simply computationally efficient. • Increased natural language processing (NLP) capabilities that allow more powerful ITS and provide psychologically valid models of language and knowledge representation. • Advances that make technology readily accessible to students. • A demonstrated need for teaching problem formulation skills in engineering curricula, as evidenced by the EC 2000 criteria .
Our goal is a design for an ITS that teaches key concepts of probability and statistics, encodes and retrieves problems, and assists students in solving problems while based on psychologically valid models of reasoning. We believe this will have the following benefits: • Students will be able to explore, adapt and augment a large database of examples with a computer-based tutor as their guide. According to cognitive science research, this should help them recognize critical similarities, represent concepts of probability and statistics, and practice solution procedures. • The ITS will help empirically test if psychological validity facilitates students' understanding
Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright © 2003, American Society for Engineering Education
Rotante, T., & Brem, S., & Hubele, N., & Runger, G., & Kennedy, K. (2003, June), Case Based Reasoning For Engineering Statistics Paper presented at 2003 Annual Conference, Nashville, Tennessee. https://peer.asee.org/11787
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