Tracking Student Performance Tool for Predicting Students EBPP in Online Courses

Iman Al-Kindi*, Zuhoor Al-Khanjari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Our motivation in this paper is to predict student Engagement (E), Behavior (B), Personality (P), and Performance (P) via designing a Tracking Student Performance Tool (TSPT) based on Moodle logfile of any selected courses. The proposed tool was develop using Python programming language along with Microsoft Excel packages for progressing data. The tool follows the predictive EBP model that focuses mainly on student's EBP and Performance. The instructor could use it to monitor the overall performance of their students during the course. The data used in this paper was a log file of the "Internet Search Strategies "course where 38 students were enrolled. The results of testing the tool show that the developed tool gives the same as manual results analysis. Analyzing Moodle log of any course using such a tool is supposed to help with the implementation of similar courses and helpful for the instructor in re-designing it in a way that is more beneficial to the students. This paper sheds light on the importance of studying student's EBP and Performance and provides interesting possibilities for improving student performance with a specific focus on designing online learning environments or contexts. This paper shows part of Ph.D. research in progress that aims to "propose a framework for smart learning behavior environment."

Original languageEnglish
Pages (from-to)140-157
Number of pages18
JournalInternational Journal of Emerging Technologies in Learning
Issue number23
Publication statusPublished - 2021


  • Ebp predictive model
  • Moodle log
  • Squ-slms framework
  • Student behavior
  • Student engagement
  • Student performance
  • Student personality

ASJC Scopus subject areas

  • Education
  • Engineering(all)


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