Dispositional Learning Analytics to Investigate Students Use of Learning Strategies

Dirk Tempelaar, Anikó Bátori, Bas Giesbers

2024

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.

Paper Citation

in Harvard Style

Tempelaar D., Bátori A. and Giesbers B. (2024). Dispositional Learning Analytics to Investigate Students Use of Learning Strategies. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-697-2, SciTePress, pages 427-438. DOI: 10.5220/0012711200003693

in Bibtex Style

@conference{csedu24,
author={Dirk Tempelaar and Anikó Bátori and Bas Giesbers},
title={Dispositional Learning Analytics to Investigate Students Use of Learning Strategies},
booktitle={Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2024},
pages={427-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012711200003693},
isbn={978-989-758-697-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Dispositional Learning Analytics to Investigate Students Use of Learning Strategies
SN - 978-989-758-697-2
AU - Tempelaar D.
AU - Bátori A.
AU - Giesbers B.
PY - 2024
SP - 427
EP - 438
DO - 10.5220/0012711200003693
PB - SciTePress