Analytics of time management strategies in a flipped classroom. Uzir, N. A., Gašević, D., Matcha, W., Jovanović, J., & Pardo, A. Journal of Computer Assisted Learning, 36(1):70–88, 2020. ZSCC: 0000000 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcal.12392![Analytics of time management strategies in a flipped classroom [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{uzir_analytics_2020,
title = {Analytics of time management strategies in a flipped classroom},
volume = {36},
copyright = {© 2019 John Wiley \& Sons Ltd},
issn = {1365-2729},
url = {http://onlinelibrary.wiley.com/doi/abs/10.1111/jcal.12392},
doi = {10/ggb957},
abstract = {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.},
language = {en},
number = {1},
urldate = {2020-06-05},
journal = {Journal of Computer Assisted Learning},
author = {Uzir, Nora'ayu Ahmad and Gašević, Dragan and Matcha, Wannisa and Jovanović, Jelena and Pardo, Abelardo},
year = {2020},
note = {ZSCC: 0000000
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcal.12392},
keywords = {flipped learning, learning analytics, self-regulated learning, time management},
pages = {70--88}
}
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