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Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11722)

Abstract

This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.

Keywords

  • Blended learning
  • Learning analytics
  • Self-Regulated Learning
  • Time management strategies

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Correspondence to Nora’ayu Ahmad Uzir , Dragan Gašević , Wannisa Matcha , Jelena Jovanović , Abelardo Pardo , Lisa-Angelique Lim or Sheridan Gentili .

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Ahmad Uzir, N. et al. (2019). Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_41

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  • DOI: https://doi.org/10.1007/978-3-030-29736-7_41

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