With the increasing use of blended learning approaches in classroom, various kinds of technologies are incorporated to provide digital teaching and learning resources to support students. These resources are often centralized in learning management systems (LMSs), which also store valuable learning data of students. The data could assist teachers in their pedagogical decision making but they are often not well utilized. This paper proposes the use of data mining and visualization techniques as learning analytics to provide a more comprehensive overview of students’ learning online based on log data from LMSs . The focus of this study is the discovery of frequent navigational patterns by sequential pattern mining techniques and the demonstration of how presentation of patterns through hierarchical clustering and sunburst visualization could facilitate the interpretation of patterns. The data in this paper were collected from a blended statistics course for undergraduate students. Copyright © 2017 Springer International Publishing AG.
|Title of host publication||Blended learning: New challenges and innovative practices: 10th International Conference, ICBL 2017, Hong Kong, China, June 27-29, 2017, Proceedings|
|Editors||Simon K.S. CHEUNG , Lam-for KWOK , Will W.K. MA , Lap-Kei LEE, Harrison YANG|
|Place of Publication||Cham|
|Publication status||Published - 2017|
CitationPoon, L. K. M., Kong, S. C., Yau, T. S. H., Wong, M., & Ling, M. H. (2017). Learning analytics for monitoring students participation online: Visualizing navigational patterns on learning management system. In S. K. S. Cheung, L.-F. Kwok, W. W. K. Ma, L.-K. Lee, & H. Yang, (Eds.), Blended learning: New challenges and innovative practices: 10th International Conference, ICBL 2017, Hong Kong, China, June 27-29, 2017, Proceedings (pp. 166-176). Cham: Springer.
- Learning analytics
- Blended learning
- Sequential pattern mining
- Hierarchical clustering
- Navigational pattern