Self-Regulated Learning Model in Educational Data Mining

Abstract

Artificial intelligence technology brings wide impacts on several dimensions. The impact on the education system is that educational technology has been disrupted, it radically changed the paradigm of learning management. Therefore, this research aimed to study the paradigm shift of the education system focusing on the deployment of artificial intelligence technology to support the learning model in the era affected by the COVID-19 pandemic. There are two research objectives: (1) to study an appropriate self-regulated learning model with data mining techniques for designing appropriate online learning management, and (2) to study the learning achievement factors of learners by applying blended learning and self-regulated learning techniques. The samples were 26 students at the University of Phayao who enrolled in the course 221203 Technology for Business Application in the 2^nd semester of the academic year 2020. The research tool is a statistical analysis and machine learning tool. It consists of analyzing pre-test scores, post-test scores, midterm scores, final scores, academic achievement, clustering analysis, and clustering performance. As a result, it found that learners had five reasonable clusters for the academic achievement learning model. The results specified the different learning styles of the learners in two dimensions including online and offline scenarios. Therefore, in future work, the researcher looks forward to performing research in the scope of identifying the suitability and the necessity of converting the face-to-face learning model to a fully online learning model.