Detecting learning strategies through process mining

John Saint, Dragan Gašević, Abelardo Pardo

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

The recent focus on learning analytics to analyse temporal dimensions of learning holds a strong promise to provide insights into latent constructs such as learning strategy, self-regulated learning, and metacognition. There is, however, a limited amount of research in temporally-focused process mining in educational settings. Building on a growing body of research around event-based data analysis, we explore the use of process mining techniques to identify strategic and tactical learner behaviours. We analyse trace data collected in online activities of a sample of nearly 300 computer engineering undergraduate students enrolled in a course that followed a flipped classroom pedagogy. Using a process mining approach based on first order Markov models in combination with unsupervised machine learning methods, we performed intra- and inter-strategy analysis. We found that certain temporal activity traits relate to performance in the summative assessments attached to the course, mediated by strategy type. Results show that more strategically minded activity, embodying learner self-regulation, generally proves to be more successful than less disciplined reactive behaviours.

Original languageEnglish
Title of host publicationLifelong Technology-Enhanced Learning
Subtitle of host publication13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3–5, 2018 Proceedings
EditorsViktoria Pammer-Schindler, Mar Perez-Sanagustin, Hendrik Drachsler, Raymond Elferink, Maren Scheffel
Place of PublicationCham Switzerland
PublisherSpringer
Pages385-398
Number of pages14
Edition1st
ISBN (Electronic)9783319985725
ISBN (Print)9783319985718
DOIs
Publication statusPublished - 2018
EventEuropean Conference on Technology Enhanced Learning (EC-TEL) 2018 - Leeds, United Kingdom
Duration: 3 Sep 20185 Sep 2018
Conference number: 13th
https://ea-tel.eu/the-13th-european-conference-on-technology-enhanced-learning-ec-tel-2018/
https://link.springer.com/book/10.1007/978-3-319-98572-5 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11082
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Technology Enhanced Learning (EC-TEL) 2018
Abbreviated titleEC-TEL 2018
CountryUnited Kingdom
CityLeeds
Period3/09/185/09/18
Internet address

Keywords

  • First order Markov models
  • Learning analytics
  • Process mining
  • Self-regulated learning
  • Temporal dynamics

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