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

• Main Authors: San Pedro, Maria Ofelia C.Z, Baker, Ryan S, Rodrigo, Ma. Mercedes T
• Format: text
• Published: Archīum Ateneo 2011
• Subjects: Carelessness; Slip; Contextual-Slip-and-Guess; Bayesian Knowledge Tracing; Cognitive Tutors; Scatterplot; Computer Sciences
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/102; https://link.springer.com/chapter/10.1007/978-3-642-21869-9_40
• Institution: Ateneo De Manila University

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).