Analytics of time management strategies in a flipped classroom

Nora’ayu Ahmad Uzir, Dragan Gašević, Wannisa Matcha, Jelena Jovanovic, Abelardo Pardo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

This study aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. The 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 analyzed using agglomerative hierarchical clustering based on the Ward's algorithm, firsst order Markov chains, and inferential statistics to detect time management tactics and strategies from students' learning activities. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace daata as manifestations of students' tactics and strategies. In addition, this study also showed that time management tactics had significant associations with academic performance.
Original languageEnglish
Title of host publicationCompanion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19)
EditorsChristopher Brooks, Rebecca Ferguson, Ulrich Hoppe
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages235-237
Number of pages2
Publication statusPublished - 2019
EventInternational Learning Analytics & Knowledge Conference 2019 - Arizona State University, Tempe, United States of America
Duration: 4 Mar 20198 Mar 2019
Conference number: 9th
https://lak19.solaresearch.org/

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2019
Abbreviated titleLAK 2019
CountryUnited States of America
CityTempe
Period4/03/198/03/19
Internet address

Keywords

  • learning analytics
  • time management
  • flipped learning
  • self-regulated learning

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