Studentlyzer for Analyzing and Visualizing E-learning Data

Zongsheng Zhao, Yating Lei, Yi Dou, Yik Him Ho, Henry C.B. Chan, Chetwyn C.H. Chan

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)147-152
Number of pages6
JournalLecture Notes in Engineering and Computer Science
Publication statusPublished - 1 Jan 2019
Event2019 International MultiConference of Engineers and Computer Scientists, IMECS 2019 - Kowloon, Hong Kong
Duration: 13 Mar 201915 Mar 2019


  • Clustering
  • E-learning
  • educational data mining
  • Online learning behavior

ASJC Scopus subject areas

  • Computer Science (miscellaneous)


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