Novel pedagogical approaches supported by digital technologies such as blended learning and flipped classroom are prevalent in recent years. To implement such learning strategies, learning resources are often put online on learning management systems. The log data on those systems provide an excellent opportunity for getting more understanding about the students through data mining techniques. In this paper, we propose to use sequential pattern mining (SPM) to discover navigational patterns on a learning platform. We attempt to address the lack of literature support about conducting SPM on Moodle. We propose a method to apply SPM that is more appropriate for mining user navigational patterns. We further propose three sequence modeling strategies for mining patterns with educational implications. Results of a study on a statistics course show the effectiveness of the proposed method and the proposed sequence modeling strategies. Copyright © 2017 Springer International Publishing AG.
|Title of host publication||Data mining and big data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27-August 1, 2017, Proceedings|
|Editors||Ying TAN, Hideyuki TAKAGI, Yuhui SHI|
|Place of Publication||Cham|
|Publication status||Published - 2017|
CitationPoon, L. K. M., Kong, S.-C., Wong, M. Y. W., & Yau, T. S. H. (2017). Mining sequential patterns of students’ access on learning management system. In Y. Tan, H. Takagi, & Y. Shi (Eds.), Data mining and big data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27-August 1, 2017, Proceedings (pp. 191-198). Cham: Springer International Publishing.
- Sequential pattern mining
- Educational data mining
- Learning management systems
- Navigational patterns