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Understanding the evolution of cognitive engagement with interaction levels in online learning environments: Insights from learning analytics and epistemic network analysis.
- Source :
- Journal of Computer Assisted Learning; Jun2023, Vol. 39 Issue 3, p984-1001, 18p
- Publication Year :
- 2023
-
Abstract
- Background: There is a strong association between interactions and cognitive engagement, which is crucial for constructing new cognition and knowledge. Although interactions and cognitive engagement have attracted extensive attention in online learning environments, few studies have revealed the evolution of cognitive engagement with interaction levels. Objectives: The study aims to automatically identify learners' interactions and cognitive engagement and then analyse the evolution of learners' cognitive engagement with interaction levels and during different stages of online learning. Methods: The participants of the study were learners who participated in an online open course. Their text data from discussion forums on five learning themes were collected. Data were analysed using text mining and ENA. Results: Learners' cognitive engagement in online learning was related to interaction levels. As learners' online interaction levels changed from surface to deep, cognitive engagement levels changed from low to high. With the continuous occurrence of deep interactions, cognitive feedback became more complex. At the social–emotional interaction level, although learners' cognitive engagement levels began to change from low to high, complex cognitive feedback was still insufficient. In addition, the analysis of the evolution of cognitive engagement during different stages of online learning showed that learners' patterns of cognitive engagement changed significantly as the learning process continued, from initially dynamic and complex to a stable development pattern. Implications: The results of the study are of theoretical significance and practical guidance for further understanding the relationship between online interaction levels and cognitive engagement as well as the process of online collaborative knowledge exploration, construction, and even connectivity. Lay Description: What is already known about this topic: Interactions and cognitive engagement are crucial for constructing new cognition and knowledge.Several studies have examined learners' interactions and cognitive engagement in online learning environments using subjective, time‐consuming analysis methods such as self‐reporting and manual coding.Although the relationship between interactions and cognitive engagement has attracted extensive attention in online learning environments, few studies have revealed the evolution of cognitive engagement with interaction levels. What this paper adds: Learning analytics can be used to automatically identify interactions and cognitive engagement in online discussion forums, which improves the classification performance.Learners' cognitive engagement in online learning is related to interaction levels. As learners' online interaction levels changed from surface to deep, cognitive engagement levels changed from low to high, and cognitive feedback became more complex, however, at the social–emotional interaction level, complex cognitive feedback was insufficient. Learners' patterns of cognitive engagement changed significantly as the learning process continued, from initially dynamic and complex to a stable development pattern during different stages of online learning. Implications for practice and/or policy: The evolution of cognitive engagement with interaction levels is helpful for knowledge construction, production and even connectivity in open learning environments.Learners should actively participate in discussions to provide complex cognitive feedback in an open online learning environment.When the discussion is off‐topic, instructors need to promptly remind learners and gradually guide it back or clear rules and requirements should be set before the topic discussion begins to minimize the contribution of low‐quality knowledge.Instructors can design tasks with high‐value attributes in an open online learning environment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664909
- Volume :
- 39
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Journal of Computer Assisted Learning
- Publication Type :
- Academic Journal
- Accession number :
- 163886547
- Full Text :
- https://doi.org/10.1111/jcal.12781