Extended Abstract book CIIDT2021 EISBN

CIIDT 2021
research question 3: Which students’ online learning behavior predict students’ performance?
The findings revealed that students’ online behavior on LMS (login time on LMS, number of
downloads, and interaction with peers) significantly predicted students’ performance (number
of exercise that students performed, and number of forums posted). This imply that these
students’ online behavior on LMS can help instructors to predict students’ performance and
provide meaningful feedback and interventions to students (Sung, Jin & Kim, 2016). This
results also reflect the benefits of regularly accessing course material and keeping pace with
the learning schedule would be helpful on students’ performance, which is in line with the
study conducted by You (2016).
CONCLUSIONS
The findings support the potential for early prediction of students’ performance based their
behavior on LMS and demonstrate the benefits of identifying significant indicators from LMS
data to facilitate successful online learning.
Keywords: Learning Management Systems, Learning Analytics, Students Online Behavior,
Students Performance
REFERENCES
Duin, A. H., & Tham, J. (2020). The Current State of Analytics: Implications for Learning
Management System (LMS) Use in Writing Pedagogy. Computers and
Composition,55, 102544.
Firat, M., & Yuzer, T. V. (2016). Learning analytics: Assessment of mass data in distance
education. International Journal on New Trends in Education and Their Implications,
7(2), 51-63.
Sung, E., Jin, S. H., & Kim, Y. (2016). Learning activities and learning behaviors for learning
analytics in e-learning environments. Educational Technology International, 17(2), 175-
202.
You, J. W. (2016). Identifying significant indicators using LMS data to predict course
achievement in online learning. The Internet and Higher Education, 29, 23-30.
You, J. W. (2015). Examining the effect of academic procrastination on achievement using
LMS data in e-learning. Journal of Educational Technology & Society, 18(3), 64-74
Yu, T., & Jo, I. H. (2014, March). Educational technology approach toward learning analytics:
Relationship between student online behavior and learning performance in higher
education. In Proceedings of the fourth international conference on learning analytics
and knowledge (pp. 269-270).
Corresponding author: Nurullizam Jamiat, Email: nurullizamj@usm.my
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