Learning Analytics intervention is an important approach that helps teachers and students improve the learning process and performance, especially for those who need more support. However, there has been limited research on developing Learning Analytics interventions based on students’ learning styles in e-learning, aiming to enhance key aspects like motivation, engagement, retention, and academic achievement. There are also fewer studies on understanding students’ learning pathways. This research designed a new Learning Analytics intervention in an e-learning environment. It examined students’ learning performances and formulated a framework of students’ learning pathways in the e-learning environment integrated with the Learning Analytics intervention based on their learning styles and preferences. An experimental design was adopted with a population of Year Two undergraduate students, employing several validated instruments. The collected data were then analyzed using descriptive analysis, content analysis, and data mining. Results indicate that most students’ learning performances were enhanced, and students exhibited different behaviors in terms of the number of log-ins, views, and posts created. Utilizing these findings, a framework of students’ learning pathways in the e-learning environment embedded with the developed Learning Analytics intervention was successfully formulated using WEKA data mining software. While the Learning Analytics intervention was designed to accommodate multiple learning styles, the framework development and detailed analysis were focused on visual learners due to the predominance of this learning style in the participant group. This framework may serve as a valuable reference point for future research.
Kew et al. (Wed,) studied this question.