Towards an Adaptive Learning Analytics Framework
Abstract: Recently, understanding and optimizing learning, and the environments in which learning occurs through learning analytics and adaptive and personalized approaches have gained more significant importance. There are many models proposed for such systems that include different variables, but as an area of research it is still in its infancy. Thus, this research study aims to reveal the most important variables to be considered in any adaptive learning analytics system model through the evaluation of expert opinion. Based on analyzed survey results, learners’ motivation levels, online tutors’ teaching strategies and methods were found to be the most important factors, whereas interactivity is favored for examination through computer logs for actual usage. Interventions should therefore address both the supporting of learners and course design for any system utilizing adaptive learning analytics.
Presider: Akira Onoue, Kyushu University