Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning
| Autor: | Wannisa Matcha, Nora'ayu Ahmad Uzir, Dragan Gašević, Abelardo Pardo, John Saint |
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| Přispěvatelé: | Saint, John, Gaševic, Dragan, Matcha, Wannisa, Uzir, Nora Ayu Ahmad, Pardo, Abelardo, 10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020 Frankfurt, Germany 23-27 March 2020 |
| Rok vydání: | 2020 |
| Předmět: |
learning analytics
self-regulated learning Process (engineering) Computer science process mining 05 social sciences Learning analytics 050301 education Process mining Sample (statistics) micro-level processes 050105 experimental psychology epistemic network analysis Mathematics education 0501 psychology and cognitive sciences Narrative Learning Management Self-regulated learning 0503 education TRACE (psycholinguistics) |
| Zdroj: | LAK |
| DOI: | 10.1145/3375462.3375487 |
| Popis: | The temporal and sequential nature of learning is receiving increasing focus in Learning Analytics circles. The desire to embed studies in recognised theories of self-regulated learning (SRL) has led researchers to conceptualise learning as a process that unfolds and changes over time. To that end, a body of research knowledge is growing which states that traditional frequency-based correlational studies are limited in narrative impact. To further explore this, we analysed trace data collected from online activities of a sample of 239 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We employed SRL categorisation of micro-level processes based on a recognised model of learning, and then analysed the data using: 1) simple frequency measures; 2) epistemic network analysis; 3) temporal process mining; and 4) stochastic process mining. We found that a combination of analyses provided us with a richer insight into SRL behaviours than any one single method. We found that better performing learners employed more optimal behaviours in their navigation through the course's learning management system. Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
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