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Student performance prediction by discovering inter-activity relations

Sahebi, S and Brusilovsky, P (2018) Student performance prediction by discovering inter-activity relations. In: UNSPECIFIED.

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© 2018 International Educational Data Mining Society. All rights reserved. Performance prediction has emerged as one of the most popular approaches to leverage large volume of online learning data. In the majority of current works, performance prediction is based on students’ past activities in graded learning resources (such as problems and quizzes), while their activities in non-graded resources (such as reading material) are ignored. In this paper, we introduce an approach that can take advantage of students’ work with non-graded learning resources, as auxiliary data, in order to predict students’ performance in graded resources. This approach can discover the hidden inter-relationships between learning resources of different types, only using student activity data. Based on our experiments, the proposed approach can significantly reduce the error of student performance prediction, compared to baseline algorithms, while discovering meaningful and surprising relationships among learning resources.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Sahebi, S
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2018
Date Type: Publication
Journal or Publication Title: Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018
Event Type: Conference
Schools and Programs: School of Computing and Information > Computer Science
Refereed: Yes
Date Deposited: 01 Jul 2019 13:05
Last Modified: 12 Oct 2019 00:55


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