Abstract—Academic monitoring is implemented at higher
learning institutions to allow students and instructors to
communicate academically, especially learning progress.
However, the system cannot monitor student performance on
an ongoing basis, such as class attendance, continuous
assessment records and assignment submissions. Personalised
learning analytics use student-generated data and analytical
models to gather learning patterns so that instructors may
advise on students’ learning. Although various studies provide
insight into the analytical framework of learning, attention to
self-regulated meaningful learning is still insufficient. This
study aims to propose a personalised learning analytics system
designed by a student that unifies the self-regulated learning
components: plan, monitor, and evaluate the learning
commitment, and activates alert of student’s achievement for
close monitoring and further intervention by the instructor. For
this reason, the procedure for analysing the learning pattern for
experiment subjects such as Internet of Things, Data Analysis
and System Management. Personalised learning analytics has
been designed to deliver an interactive learning analytics
environment that stimulates students to focus on the
achievement of problem-solving skills and enhance the
instructor’s decision to support students’ concern.
Index Terms—Learning analytics, personalised learning,
self-regulated meaningful learning.
Muhammad Izzat Izzuddin bin Zainuddin and Hairulliza Mohamad Judi
are with the Faculty of Information Science and Technology, Universiti
Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
(e-mail: hmj.ftsm@gmail.com).
Cite: Muhammad Izzat Izzuddin bin Zainuddin and Hairulliza Mohamad Judi, "Personalised Learning Analytics: Promoting Student’s Achievement and Enhancing Instructor’s Intervention in Self-regulated Meaningful Learning," International Journal of Information and Education Technology vol. 12, no. 11, pp. 1243-1247, 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).