Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems

Abstract

Self-regulated learning (SRL) is a very significant ability for students in the learning process, and SRL strategies are used to assist students in learning efficiently in higher education. Many SRL environments face challenges and barriers, including less immediate feedback and guidance, a high level of self-personalization, environment, and resources from teachers or learner designers. Hence, in this paper, the artificial intelligence framework for self-regulated learning (AIF-SRL) overcame the SRL environment's challenges in higher education. Self-regulated students are active participants in their learning and may select from a strategic portfolio and monitor their progress towards the objective. The proposed artificial intelligence-based solution has been used to monitor student behavior through students' feedback and responses. The experimental results show that the proposed AIF-SRL method improves the student's self-evaluation, self-regulation behavior, self-efficacy, learning gain, and self-satisfaction compared to other existing methods.