Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques
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 bl...
| 出版年: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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| 第一著者: | |
| フォーマット: | Conference paper |
| 言語: | English |
| 出版事項: |
Springer Verlag
2019
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072948958&doi=10.1007%2f978-3-030-29736-7_41&partnerID=40&md5=bd41f41c3e2bb645a45f2e71c460c5c7 |
| 要約: | 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. © 2019, Springer Nature Switzerland AG. |
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| ISSN: | 3029743 |
| DOI: | 10.1007/978-3-030-29736-7_41 |