A Sustainable Learning Behavior Analytics Framework for Customized Learning Management Systems

Abstract

The Learning Management System (LMS) has emerged as a key platform for e-learning and as an agent to create massive amounts of data about learners' interactions, which can be used for educational decision making. Yet, many LMS systems that are in place today only capture students' behaviors, but fail to make them relevant to the context of behavioral analysis. Based on the above, this study is proposing the Enhanced Learning Behavior Analytics Framework (LBAF) that could be embedded in a customized LMS to systematically collect, organize and analyze the learners' behavior data during the online learning process. The proposed framework was designed adhering to the ADDIE instructional design model and involves several behavioral indicators such as; course participation, content exploration, interaction with the video, involvement in the discussion forum, performance in assessment, and engagement with the learner. It was implemented and evaluated by analyzing the behavioral data collected from 45 undergraduate students, and by conducting structured evaluation questionnaires with students and instructors. Based on the experimental results, the proposed framework is able to support the monitoring of the learner's engagement and the assessment of instruction and analysis of the behavior of the learner in the customized LMS is successful. The students' and instructors' perceptions of the usability of the system and flexibility of the communication and overall learning experience were positive. The proposed framework offers a viable solution to integrate learning behavior analytics into customized LMS for evidence-based instruction decision making, effective online learning environment, and sustainable digital education in line with SDG 4 (Quality Education).