A multi-modal study into students’ timing and learning regulation: time is ticking
Abstract
Purpose This empirical study aims to demonstrate how the combination of trace data derived from technology-enhanced learning environments and self-response survey data can contribute to the investigation of self-regulated learning processes. Design/methodology/approach Using a showcase based on 1,027 students’ learning in a blended introductory quantitative course, the authors analysed the learning regulation and especially the timing of learning by trace data. Next, the authors connected these learning patterns with self-reports based on multiple contemporary social-cognitive theories. Findings The authors found that several behavioural facets of maladaptive learning orientations, such as lack of regulation, self-sabotage or disengagement negatively impacted the amount of practising, as well as timely practising. On the adaptive side of learning dispositions, the picture was less clear. Where some adaptive dispositions, such as the willingness to invest efforts in learning and self-perceived planning skills, positively impacted learning regulation and timing of learning, other dispositions such as valuing school or academic buoyancy lacked the expected positive effects. Research limitations/implications Due to the blended design, there is a strong asymmetry between what one can observe on learning in both modes. Practical implications This study demonstrates that in a blended setup, one needs to distinguish the grand effect on learning from the partial effect on learning in…
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A substantial number of studies demonstrated strong theoretical grounding by integrating established engagement frameworks into their analyses.
“A substantial number of studies demonstrated strong theoretical grounding by integrating established engagement frameworks into their analyses (e.g., Tempelaar et al., 2018;Wong & Chong, 2018).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Tempelaar et al., 2018
Tempelaar et al. (2018) utilised the Epistemic Emotion Scales and Achievement Emotions Questionnaire to explore the interplay between learning cognition, behaviour, and regulation among undergraduate students.
“For instance, Tempelaar et al. (2018) utilised the Epistemic Emotion Scales and Achievement Emotions Questionnaire to explore the interplay between learning cognition, behaviour, and regulation among undergraduate students.”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Tempelaar et al., 2018
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References 23 · Cited by 12
- Authors
- Tempelaar, D., Rienties, B., & Nguyen, Q.
- Affiliations
- Maastricht University (NL)
- The Open University (GB)
- Venue
- Interactive Technology and Smart Education
- Publisher
- Emerald
- Published
- 2018-07-01
- Volume
- 15
- Issue
- 4
- Pages
- 298-313
- ISSN
- 1741-5659 (ISSN-L)
- OpenAlex
- W2796449575
- Cited by
- 12
- References
- 23
- Citations by year
- 2025: 2 · 2024: 2 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 4
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Technology and Smart Education (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion)
- Open Research Online - ORO (The Open University) (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Online and Blended Learning
- Keywords
- student self-regulation, trace data, blended learning, technology-enhanced learning, learning dispositions, self-sabotage, disengagement, academic buoyancy, effort investment
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