Proceedings of the 7th International Academic Conference on Education, Teaching and Learning
Year: 2024
DOI:
[PDF]
Designing Semantic Tutoring Maps for Enhancing Peer Learning through Lesson Preparation
Yu Cui, Prof. Peter Reimann
ABSTRACT:
Advancements in Natural Language Processing technology present significant opportunities to enhance student learning through peer tutoring. While there is substantial research on the effectiveness of peer tutoring in learning science, few studies focus on the lesson preparation stage of peer tutoring. This study utilises Semantic Web technologies to organise tutoring maps, aiming to develop an innovative learning artefact to address this research gap. Adopting a design-based research methodology, this study first provides a literature review regarding the application of semantic technology in education and peer tutoring preparation. Subsequently, a tutoring map application based on a semantic database is developed, encompassing the organisation and storage of tutoring lesson plans using Resource Description Framework (RDF) technology, and the querying and feedback on lesson plan quality based on SPARQL Protocol and RDF Query Language (SPARQL) technology. This paper describes the theoretical basis and design rationale by which student tutors’ learning is improved through structured tutoring preparation. This includes enhancing cognitive and meta-cognitive abilities in designing learning activities, raising questions, anticipating answers, and providing various levels of guidance responses to incorrect answers. The paper concludes with recommendations for educational researchers and practitioners. Future research will focus on empirical studies of peer tutoring in scientific fields to verify the validity and adaptability of the design, further facilitating peer learning by tutoring and collective knowledge building.
keywords: learning science, peer tutoring, prepare for tutoring, semantic web, tutor training