With the increasing popularity of e-learning in higher education institutions, there is a need to develop data analytics tools to analyze e-learning data, student learning behavior and student performance. In recent years, there has been growing interest in educational data mining, which can provide useful insights into student learning behavior, providing holistic analysis. This paper presents an online data analytics tool called Studentlyzer, which applies data mining to analyze student data. It can cluster student datasets using K-means clustering, and visualize the graphical results through a web browser. Two real-world student e-learning datasets, the Open University Learning Analytics Dataset (OULAD) and Educational Processing Mining (EPM) dataset, were used to demonstrate Studentlyzer’s usefulness. The results provide valuable insights about students. In general, Studentlyzer can help identify students who are similar (e.g., with similar study behavior) and provide useful information about student performance and student behavior (e.g., their correlation). Copyright © 2019 International Association of Engineers.
|Title of host publication||Proceedings of the International MultiConference of Engineers and Computer Scientists 2019|
|Publication status||Published - 2019|
CitationZhao, Z., Lei, Y., Dou, Y., Ho, Y. H., Chan, H. C. B., & Chan, C. C. H. (2019). Studentlyzer for analyzing and visualizing e-learning data. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2019. Retrieved from http://www.iaeng.org/publication/IMECS2019/
- Educational data mining
- Online learning behavior