Analytics of time management strategies in a flipped classroom
Open accessAbstract
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N= 1,134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analysed using agglomerative hierarchical clustering based on Ward's algorithm, first order Markov chains, and inferential statistics to (a) detect time management tactics and strategies from students' learning activities and (b) analyse the effects of personalized analytics‐based feedback on time management. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace data as manifestations of students' tactics and strategies. The study also showed that time management tactics had significant associations with academic performance and were associated with different interventions in personalized analytics‐based feedback.
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The idea was that by working on assignments during class there would be more accountability as evaluations will take place during instructional time and under instructional supervision with no opportunity or incentive for dishonesty.
“The idea was that by working on assignments during class there would be more accountability as evaluations will take place during instructional time and under instructional supervision with no opportunity or incentive for dishonesty [1, 6].”
From Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines (2025) · cites this paper as [1]
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“It makes use of trace data to reveal individual differences in time management tactics and strategies and how these relate to the students’ learning achievements (Broadbent & Poon, 2015), especially in flipped classroom setting.”
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- Authors
- Ahmad Uzir, N., Gašević, D., Matcha, W., Jovanović, J., & Pardo, A.
- Affiliations
- School of Informatics The University of Edinburgh Edinburgh UK
- Monash University (AU)
- University of Edinburgh (GB)
- University of Belgrade (RS)
- University of South Australia (AU)
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2019-10-10
- Volume
- 36
- Issue
- 1
- Pages
- 70-88
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W2980121951
- Cited by
- 129
- References
- 97
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 21 · 2023: 22 · 2022: 39 · 2021: 18 · 2020: 8
- FWCI
- 17.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.solaresearch.org
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- HAL (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by)
- Repository of the University of Belgrade Faculty of Organizational Sciences (RFOS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Innovative Teaching Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- time management strategies, flipped classroom, learning analytics, trace data, student learning activity, academic performance, computer engineering education
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