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2021 | OriginalPaper | Buchkapitel

Using Epistemic Networks to Analyze Self-regulated Learning in an Open-Ended Problem-Solving Environment

verfasst von : Luc Paquette, Theodore Grant, Yingbin Zhang, Gautam Biswas, Ryan Baker

Erschienen in: Advances in Quantitative Ethnography

Verlag: Springer International Publishing

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Abstract

The micro-level analyses of how students’ self-regulated learning (SRL) behaviors unfold over time provides a valuable framework for understanding their learning processes as they interact with computer-based learning environments. In this paper, we use log trace data to investigate how students self-regulate their learning in the Betty’s Brain environment, where they engage in three categories of open-ended problem-solving actions: information seeking, solution construction and solution assessment. We use Epistemic Network Analysis (ENA) to provide us with an overall understanding of the co-occurrences between action types both within and between the three action categories. Comparisons of epistemic networks generated for two groups of students, those with low and high performance, provided us with insights into their self-regulated behaviors.

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Vorheriges Kapitel Socio-semantic Network Analysis of Knowledge-Creation Discourse on a Real-Time Scale

Nächstes Kapitel The Value of Epistemic Network Analysis in Single-Case Learning Analytics: A Case Study in Lifelong Learning

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Titel: Using Epistemic Networks to Analyze Self-regulated Learning in an Open-Ended Problem-Solving Environment
verfasst von: Luc Paquette
Theodore Grant
Yingbin Zhang
Gautam Biswas
Ryan Baker
Verlag: Springer International Publishing
Buch: Advances in Quantitative Ethnography
Print ISBN: 978-3-030-67787-9

Electronic ISBN: 978-3-030-67788-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-67788-6_13

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