Data-driven problem based learning: enhancing problem based learning with learning analytics
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Data‑driven problem based learning: enhancing problem based learning with learning analytics Maria Zotou1 · Efthimios Tambouris1 · Konstantinos Tarabanis1 Accepted: 10 September 2020 © Association for Educational Communications and Technology 2020
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. Keywords Problem based learning · Learning analytics · PBL model · Course design · Technology enhanced learning
Introduction Problem based learning (PBL) is a well-established learning strategy that enables active participation of students who “learn by doing” and supports the development of transversal and lifelong learning skills (Sohmen 2020; Zhou and Zhu 2019). When PBL is reinforced with the utilization of collaborative Web technologies in blended settings, termed PBL2.0 (Tambouris et al. 2012), students can use diverse tools in order to more effectively perform * Maria Zotou [email protected] Efthimios Tambouris [email protected] Konstantinos Tarabanis [email protected] 1
University of Macedonia, Egnatia 156 Street, Thessaloniki, Greece
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the required tasks in solving their problems, which leads to the generation of large amounts of data (Ünal 2019; Zotou 2015). However, educators can rarely make sense of what this data entails for the progress of the course and what relevant decisions can be made. The application of PBL in courses faces other challenges as well, since educators usually feel it is not that easy to change their teaching style to the PBL format (Chen et al. 2020). During this process, they are usually unsure of each student’s learning progress, contribution to the group work and need for assistance. This limits their ability to provide fair assessment, ongoing scaffolding and reduce the drop-out numbers (Chen et al. 2016). An interesting emerging field that could address these challenges is learning analytics
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