Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics
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).
San Pedro M.O.C.Z., Baker R.S.J.., Rodrigo M.M.T. (2011) Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg