Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathema

A student is said to have committed a careless error when a student’s answer is wrong despite the fact that he or she knows the answer (Clements, 1982). In this paper, educational data mining techniques are used to analyze log files produced by a cognitive tutor for Scatterplots to derive a model and detector for carelessness. Bayesian Knowledge Tracing and its variant, the Contextual-Slip-and-Guess Estimation, are used to model and predict carelessness behavior in the Scatterplot Tutor. The study examines as well the robustness of this detector to a major difference in the tutor’s interface, namely the presence or absence of an embodied conversational agent, as well as robustness to data from a different school setting (USA versus Philippines).

• Type: Journal
• Year: 2011
• Where: AIED
• Authors: Maria Ofelia Clarissa Z. San Pedro, Ryan Shaun Joazeiro de Baker, Ma. Mercedes T. Rodrigo
• Keywords: AIED 2011, Artificial Intelligence, Careless Error, Cognitive Tutor, Data Mining Techniques