Multistrategy Discovery and Detection of Novice Programmer Errors

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.

• Type: Journal
• Year: 2000
• Where: ML
• Authors: Raymund Sison, Masayuki Numao, Masamichi Shimura
• Keywords: Machine Learning, ML 2000, Novice, Novice Behavior, Unsupervised Incremental Multistrategy