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


Maria Ofelia Clarissa San Pedro, Ryan S.J.d. Baker and Ma. Mercedes Rodrigo

Paper type: 
Full paper
16. 13:30-15:00, Friday 1 July


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).


Carelessness, Slip, Contextual-Slip-and-Guess, Bayesian Knowledge Tracing, Cognitive Tutors, Scatterplot.