TY - JOUR
T1 - Extracting Student Patterns from Log File Moodle Course: A Case Study
T2 - A case study
AU - Al-Kindi, Iman Rashid
AU - Al-Khanjari, Zuhoor
AU - Jamoussi, Yessine
N1 - Publisher Copyright:
© 2022, Institute of Advanced Engineering and Science. All rights reserved.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - 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.
AB - 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.
KW - Log file
KW - Moodle
KW - Pattern
KW - Student engagement
KW - Student personality
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UR - http://www.scopus.com/inward/citedby.url?scp=85133183145&partnerID=8YFLogxK
U2 - 10.11591/ijere.v11i2.23242
DO - 10.11591/ijere.v11i2.23242
M3 - Article
AN - SCOPUS:85133183145
SN - 2252-8822
VL - 11
SP - 917
EP - 926
JO - International Journal of Evaluation and Research in Education
JF - International Journal of Evaluation and Research in Education
IS - 2
M1 - ISSN: 2252-8822
ER -