Knowledge or Gaming? Cognitive Modelling Based on Multiple-Attempt Response

Publication Type:
Conference Proceeding
Citation:
2017, pp. 321 - 329
Issue Date:
2017
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Recent decades have witnessed the rapid growth of intelli- gent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplored. To this end, we propose a Knowledge Plus Gaming Response Model (KPGRM) based on multiple-attempt responses. Specifi- cally, we first measure the explicit gaming factor in each multiple-attempt response. Next, we utilize collaborative filtering methods to infer the implicit gaming factor of one- attempt responses. Then we model student learning cog- nitively by considering both gaming and knowledge factors simultaneously based on a signal detection model. Extensive experiments on two real-world datasets prove that KPGRM can model student learning more effectively as well as obtain a more reasonable analysis.
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