Understanding the evolution of cognitive engagement with interaction levels in online learning environments: Insights from learning analytics and epistemic network analysis

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.

Using Epistemic Network Analysis to Explore Primary School Students’ Computational Thinking in Pair Programming Learning

“…The subtracted discourse network can represent the differences in the two clusters’ category connection patterns (Guo et al, 2023; Sun et al, 2022). Figures 6–8 represent the comparison of students’ CT structure among the three learning topics.…”

“…Thus, we set the window size to 7 to analyze the group discourse. In addition, to further compare the differences in CT skills between different projects and groups, based on the views of Guo et al (2023) and Paquette et al (2021), we used a two-sample Mann-Whitney U test to analyze the distribution of ENA spatial projection points of CT skills.…”

“…ENA can effectively detect differences in connections between codes with similar frequencies compared with automated content analysis (Ba et al, 2022) and quantitative content analysis (Csanadi et al, 2018). It also goes beyond traditional static quantitative analysis to capture progressive relationships between different variables, such as cognition and interaction (Guo et al, 2023). For example, Lye and Koh (2014) suggested that recording the thought processes of students' verbal expression in the programming process and combining them with students' programming processes can fully reflect the input thought processes and ability levels of each student in the group when they participate in collaborative programming.…”

“…ENA as a discourse analysis technique is used to construct computer-based models that illustrate the co-occurrences of codes in relevant discourse between collaborating individuals (Swiecki & Shaffer, 2020). ENA has been shown to be effective in terms of learning process analysis (Guo et al, 2023; Tan et al, 2022). Guo et al (2023) used ENA to reveal the evolution process of learners’ cognitive engagement with different levels of interaction in online learning.…”

“…ENA has been shown to be effective in terms of learning process analysis (Guo et al, 2023; Tan et al, 2022). Guo et al (2023) used ENA to reveal the evolution process of learners’ cognitive engagement with different levels of interaction in online learning. Tan et al (2022) analyzed the development trajectory of learners’ co-cognitive agency in online collaborative learning environments via ENA.…”