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Multistrategy discovery and detection of novice programmer errors

delete2000-01-01
delete24
delete
OA
AI
S
Sison, RC
M
Masayuki Numao
M
Masamichi Shimura
DOI：10.1023/A:1007690108308delete
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Abstract

Abstract

En 中文
Detecting and diagnosing errors in novice behavior is an important student modeling task. In this paper, we describe MEDD, an unsupervised incremental multistrategy system for the discovery of classes of errors from, and their detection in, novice programs. Experimental results show that MEDD can effectively detect and discover misconceptions and other knowledge-level errors that underlie novice Prolog programs, even when multiple errors are enmeshed together in a single program, and when the programs are presented to MEDD in a different order.
Keywords:
multistrategy learning
unsupervised learning
conceptual clustering
student modeling
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Machine Learning cover
Machine Learning
IF:
2.9
Papers:
2.6K
Citations:
3.4W

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