Recent studies have shown that students follow stable behavioral patterns while learning in online educational systems. These behavioral patterns can further be used to group the students into different clusters. However, as these clusters include both high- and low-performance students, the relation between the behavioral patterns and student performance is yet to be clarified. In this work, we study the relationship between students’ learning behaviors and their performance, in a self-organized online learning system that allows them to freely practice with various problems and worked examples. We represent each student’s behavior as a vector of highsupport sequential micro-patterns. Then, we discover both the prevalent behavioral patterns in each group and the shared patterns across groups using discriminative non-negative matrix factorization. Our experiments show that we can successfully detect such common and specific patterns in students’ behavior that can be further interpreted into student learning behavior trait patterns and performance patterns.
Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors
Modern online learning platforms offer a wealth of learning content while leaving the choice of content for study and practice to the learner. Recent work has demonstrated that many students use inefficient learning strategies that lead to lower performance in this context. The ability to detect inefficient learning behavior by monitoring learning data opens a way to timely intervention that could lead to better learning and performance. In this work, we propose SB-DNMF, a structure-based discriminative non-negative matrix factorization model aimed to distinguish between common and distinct learning behavior patterns of low- and high-learning gain students. Our model can discover latent groups of students' behavioral micro-patterns while accounting for the structural similarities between these micro-patterns based upon a weighted edit-distance measure. Our experiments demonstrate that SB-DNMF can find meaningful latent factors that are associated with students' learning gain and can cluster the behavioral patterns into common (trait), and performance-related groups.
- Award ID(s):
- 1755910
- Publication Date:
- NSF-PAR ID:
- 10296475
- Journal Name:
- 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
- Page Range or eLocation-ID:
- 283 to 290
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/WIIAT50758.2020.00041
