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: email@example.com).