A Rule-based Learning Diagnosis Scheme to Scaffold Self-Regulated Learning for Hypermedia-based Learning

Abstract

Hypermedia-based learning (HBL) has shown the potential capabilities to improve learners' learning performance in terms of complicated subjects and skills, but most of learners are difficult to learn well in this kind of learning environment due to the lack of self-regulated learning abilities. The manually adaptive scaffoldings, which are the most effective approach to facilitate learners to regulate their learning strategies, are quite time and work consuming for teachers, and most of existing intelligent tutoring systems without modeling diagnosis knowledge of teachers are not able to provide learners with the adaptive scaffolding concerning learning barriers and corresponding reasons. Therefore, in this paper, a novel Rule-based Learning Diagnosis Scheme (RLDS) is proposed to automatically support the adaptive scaffoldings for learners in HBL environments, where rule base and concept ontology based upon teachers' diagnosis knowledge are developed to represent the subject concepts in order to analyze learning barriers. The experimental results showed that learners who studied with adaptive scaffoldings of RLDS had significantly higher scores in post-test than those who studied without adaptive scaffoldings.