Designing and Incorporating Personalized Learning Analytics: Examining Self-Regulated Meaningful Learning

Abstract

Education in the normal era post-pandemic implements flexible learning to allow students to choose learning places and steps by integrating students and technology to enable new possibilities aimed at effective learning. However, students encounter tremendous challenges of losing learning focus for not giving the necessary commitment or enough attention to deal with learning tasks. This learning problem may create a severe issue if not detected from the beginning and addressed systematically. Learning analytics can monitor learning progress and identify the learning problems of individual students. Several previous studies focus on the analytical framework of learning, but attention to meaningful learning and the development of problem-solving skills is still insufficient. This paper aims to propose a self-regulated meaningful analytic learning framework that promotes problem-solving skills. The framework aims to guide educators and administrators in the new normal in implementing personalised learning analytics in self-regulated meaningful learning. The employment of meaningful learning strategies supported by learning analytics help students prepare the skills needed to match their individual needs.