Extracting student patterns from log file Moodle course: A case study

Iman Rashid Al-Kindi*, Zuhoor Al-Khanjari, Yessine Jamoussi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper introduces a set of extracted factors from Moodle log file of the selected course as a case study that aims to capture student Engagement (E), Behavior (B), Personality (Pers) and Performance (P). The factors are applied to identify students’ EBPersP with different course activities. The data set used in this paper was selected from the "Introduction to Computer Science" online course that captures 273,906 records as a log file for 29 students, delivered in Spring 2020. The paper also tries to show whether there is a relationship between student engagement, behavior and personality and their performance. Results show different patterns of students’ interactions with course contents, activities, and assessments. Specifically, our findings highlight that students' EBPersP could be extracted from Moodle log files. In addition, the extracted factors could assist instructors on how to focus more on students with low and average performance, giving them more attention to enhancing their performance.

Original languageEnglish
Pages (from-to)917-926
Number of pages10
JournalInternational Journal of Evaluation and Research in Education
Issue number2
Publication statusPublished - Jun 2022


  • Log file
  • Moodle
  • Pattern
  • Student engagement
  • Student personality

ASJC Scopus subject areas

  • Education


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