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 an...
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| Main Authors: | , , |
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| Format: | text |
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Archīum Ateneo
2011
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| Subjects: | |
| Online Access: | https://archium.ateneo.edu/discs-faculty-pubs/102 https://link.springer.com/chapter/10.1007/978-3-642-21869-9_40 |
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| Institution: | Ateneo De Manila University |
| Summary: | 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). |
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