Using recommender systems to promote self-regulated learning in online education settings: current knowledge gaps and suggestions for future research

Self-regulated learning (SRL) plays a significant role in promoting academic success in online education. In recent years, attention has focused on using new techniques to promote SRL—one of which is the recommender system. However, there has been little discussion of the actual effects of using recommender systems to facilitate SRL skills among online learners. This paper aims to elucidate the role that recommender systems play in assisting learners to gain self-regulation skills. The main topics addressed in this paper are as follows: (1) SRL strategies that are supported by recommender systems, as well as the techniques used by these recommenders to promote SRL strategies; and (2) evaluations conducted on the use of recommender systems and results. Our analysis of 20 empirical articles indicates that various features of recommender systems were designed to promote SRL strategies in different phases, and students were generally positive about using such systems to help them self-regulate. Five key knowledge gaps related to existing research on SRL recommender systems were identified. The conclusions suggest that future studies could be improved by demonstrating a more comprehensive understanding of the design of recommender systems, as well as by placing more emphasis on the evaluation process.