Abstract
What can we learn from dispositional learning analytics about how first-year business and economics students approach their introductory math and stats course? This study aims to understand how students engage with learning tasks, tools, and materials in their academic pursuits. It uses trace data, initial assessments of students' learning attitudes, and survey responses from the Study of Learning Questionnaire (SLQ) to analyse their preferred learning strategies. An innovative aspect of this research is its focus on clarifying how learning
attitudes influence and potentially predict the adoption of specific learning strategies. The data is examined to detect clusters that represent typical patterns of preferred strategies, and relate these profiles to students' learning dispositions. Information is collected from two cohorts of students, totalling 2400 first-year students. A pivotal conclusion drawn from our research underscores the importance of adaptability, which involves the capacity to modify preferred learning strategies based on the learning context. While it is crucial to educate
our students in deep learning strategies and foster adaptive learning mindsets and autonomous regulation of learning, it is equally important to acknowledge scenarios where surface strategies and controlled regulation may offer greater effectiveness.
attitudes influence and potentially predict the adoption of specific learning strategies. The data is examined to detect clusters that represent typical patterns of preferred strategies, and relate these profiles to students' learning dispositions. Information is collected from two cohorts of students, totalling 2400 first-year students. A pivotal conclusion drawn from our research underscores the importance of adaptability, which involves the capacity to modify preferred learning strategies based on the learning context. While it is crucial to educate
our students in deep learning strategies and foster adaptive learning mindsets and autonomous regulation of learning, it is equally important to acknowledge scenarios where surface strategies and controlled regulation may offer greater effectiveness.
Original language | English |
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Title of host publication | Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2 |
Editors | Oleksandra Poquet, Alejandro Ortega-Arranz, Olga Viberg, Irene-Angelica Chounta, Bruce McLaren, Jelena Jovanovic |
Publisher | Scitepress - Science And Technology Publications |
Pages | 427-438 |
Number of pages | 12 |
Volume | 2 |
ISBN (Print) | 978-989-758-697-2 |
DOIs | |
Publication status | Published - 11 Apr 2024 |
Publication series
Series | Proceedings of the International Conference on Computer Supported Education, CSEDU |
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ISSN | 2184-5026 |
Keywords
- dispositional learning analytics
- learning strategies
- self-regulated learning
- problem-based learning
- higher education