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.
|Publication status||Published - Jun 2017|
|Event||The International Conference on Blended Learning 2017 (ICBL2017): "Blended Learning : New Challenges and Innovative Practices" - City University of Hong Kong, Hong Kong|
Duration: 27 Jun 2017 → 29 Jun 2017
|Conference||The International Conference on Blended Learning 2017 (ICBL2017): "Blended Learning : New Challenges and Innovative Practices"|
|Period||27/06/17 → 29/06/17|
CitationPoon, L. K. M., Kong, S. C., Yau, T. S. H., Wong, M., & Ling, M. H. (2017, June). Learning analytics for monitoring students participation online: Visualizing navigational patterns on learning management system. Paper presented at the International Conference on Blended Learning (ICBL 2017), City University of Hong Kong, Hong Kong, China.
- Sequential pattern mining
- Hierarchical clustering
- Navigational pattern
- Learning analytics
- Blended learning