Sahebi, S and Brusilovsky, P
(2018)
Student performance prediction by discovering inter-activity relations.
In: UNSPECIFIED.
Abstract
© 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.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[img]](https://d-scholarship.pitt.edu/style/images/fileicons/text_plain.png)
![[feed]](/images/feed-icon-32x32.png){kind=icon}