Transferring effective learning strategies across learning contexts matters: A study in problem-based learning
Keywords:learning strategies, problem-based learning (PBL), learning analytics, sequence mining, process mining
Learning strategies are important catalysts of students’ learning. Research has shown that students with effective learning strategies are more likely to have better academic achievement. This study aimed to investigate students’ adoption of learning strategies in different course implementations, the transfer of learning strategies between courses and relationship to performance. We took advantage of recent advances in learning analytics methods, namely sequence and process mining as well as statistical methods and visualisations to study how students regulate their online learning through learning strategies. The study included 81,739 log traces of students’ learning related activities from two different problem-based learning medical courses. The results revealed that students who applied deep learning strategies were more likely to score high grades, and students who applied surface learning strategies were more likely to score lower grades in either course. More importantly, students who were able to transfer deep learning strategies or continue to use effective strategies between courses obtained higher scores, and were less likely to adopt surface strategies in the subsequent course. These results highlight the need for supporting the development of effective learning strategies in problem-based learning curricula so that students adopt and transfer effective strategies as they advance through the programme.
Implications for practice or policy:
- Teachers need to help students develop and transfer deep learning as they are directly related to success.
- Students who continue to use light strategies are more at risk of low achievement and need to be supported.
- Technology-supported problem-based learning requires more active scaffolding and teachers’ support beyond “guide on the side” as in face-to-face.
How to Cite
Copyright (c) 2023 Mohammed Saqr, Wannisa Matcha, Nora'ayu Ahmad Uzir, Jelena Jovanovic, Dragan Gašević, Sonsoles López-Pernas
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