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ERIC Number: ED499172
Record Type: Non-Journal
Publication Date: 2006-Apr
Pages: 29
Abstractor: Author
An Exploratory Study Examining the Feasibility of Using Bayesian Networks to Predict Circuit Analysis Understanding
Chung, Gregory K. W. K.; Dionne, Gary B.; Kaiser, William J.
Online Submission, Paper presented at the Annual Meeting of the National Council on Measurement in Education (NCME) (San Francisco, CA, Apr 2006)
Our research question was whether we could develop a feasible technique, using Bayesian networks, to diagnose gaps in student knowledge. Thirty-four college-age participants completed tasks designed to measure conceptual knowledge, procedural knowledge, and problem-solving skills related to circuit analysis. A Bayesian network was used to model the knowledge dependencies among the circuit analysis concepts. Preliminary results suggested that the Bayesian network was generally working as intended. When high- and low-performing groups were formed on the basis of posterior probabilities, significant group differences were found favoring the high-performing group with respect to circuit definitions and circuit analysis problems, for both actual and self-assessments, and higher major GPA. The Bayesian network was able to predict participants' performance on a problem-solving item on average 75% of the time. The findings of this study are promising for our long-term goal of developing scalable and feasible online automated reasoning techniques to diagnose student knowledge gaps. (Contains 12 tables and 2 figures.) [Appended are: (1) Node-Voltage Analysis Problem-Solving Procedure (Kaiser, 2003); and (2) Bayesian Network.]
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A