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By Sujith M. Gowda, Jonathan P. Rowe, Min Chi and Kenneth R. Koedinger

Abstract

Over the past several years, several extensions to Bayesian knowledge tracing have been proposed in order to improve predictions of students ’ in-tutor and post-test performance. One such extension is Contextual Guess and Slip, which incorporates machine-learned models of students ’ guess and slip behaviors in order to enhance the overall model’s predictive performance [Baker et al. 2008a]. Similar machine learning approaches have been introduced in order to detect specific problem-solving steps during which students most likely learned particular skills [Baker, Goldstein, and Heffernan in press]. However, one important class of features that have not been considered in machine learning models used in these two techniques is metrics of item and skill difficulty, a key type of feature in other assessment frameworks [e.g Hambleton, Swaminathan, & Rogers, 1991; Pavlik, Cen, & Koedinger 2009]. In this paper, a set of engineered features that quantify skill difficulty and related skill-level constructs are investigated in terms of their ability to improve models of guessing, slipping, and detecting moment-by-moment learning. Supervised machine learning models that have been trained using the new skill-difficulty features are compared to models from the original contextual guess and slip and moment-by-moment learning detector work. This includes performance comparisons for predictin

Topics: Categories and Subject Descriptors, I 2.7 [Artificial Intelligence] General Terms, Educational Data Mining, Cognitive Tutor, Feature Engineering, Post-test prediction, Momentby-Moment Learning, Contextual Guess, Contextual Slip, Bayesian Knowledge-Tracing Additional Key Words and Phrases
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.578
Provided by: CiteSeerX
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