A rule‐based self‐regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software

Abstract Many studies indicate that most students find it difficult to learn effectively in computer‐based learning environments because of a lack of self‐regulated learning (SRL) abilities. Therefore, based on the design approach of the rule‐based system, this paper proposes a rule‐based self‐regulated learning assistance scheme (SRL‐RuAS) to intelligently facilitate personalized learning with SRL‐based adaptive scaffolding support for learning computer software. In the SRL‐RuAS scheme, SRL‐based adaptive scaffolding strategies (SRL‐AS) are defined according to SRL behaviors, the subject knowledge is formatted using the concept ontology technique, the relations of learning resources and the portfolio of students are modeled by the proposed learning analysis data model, and the learning analysis rules of adaptive scaffolding are defined according to SRL‐AS. Consequently, an intelligent learning environment with SRL‐RuAS can intelligently give learning support to actively assist students in understanding the learning status and guiding learning improvement. Hence, the learning process with SRL‐based assistance is able to improve learning motivation and effectiveness. Experimental results of a case study for learning computer software show that the SRL‐RuAS scheme is beneficial for learning achievement and the satisfaction of students, and its scaffolding strategies and application domain can thus be manageable and extended because of the rule‐based system design.