Data-driven problem based learning: enhancing problem based learning with learning analytics

Abstract

Problem based learning (PBL) supports the development of transversal skills and could underpin the training of a workforce competent to withstand the constant generation of new information. However, the application of PBL is still facing challenges, as educators are usually unsure how to structure student-centred courses, how to monitor students’ progress and when to provide guidance. Recently, the analysis of educational data, namely learning analytics (LA), has brought forth new perspectives towards informative course monitoring and design. However, existing research shows that limited studies have combined PBL with LA to explore their potential in offering data-driven, student-centred courses. This paper presents a framework, termed PBL_LA, that aims to address this gap by combining PBL with LA. The framework is populated from the literature and discussions with PBL and LA experts. The paper also presents results from redesigning, delivering and assessing ten courses in different disciplines and countries using the proposed framework. Results showed positive feedback on all different testing settings, exhibiting reliability of the framework and potential across countries, disciplines and sectors.