Towards automated analysis of cognitive presence in MOOC discussions

Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser; Zhu, Zexuan

doi:10.1145/3375462.3375473

Cited by 16 publications

(49 citation statements)

References 18 publications

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“…Recent work has identified cue phrases and dialogue features that are correlated with specific framework labels [8,10], and relationships between the labels and topics extracted from the course content [27]. Although manual content analysis is slow and expensive, several recent studies have achieved promising results automating the labelling process using these frameworks [1,9,11,20,21,23,25].…”

Section: Background 21 Analysis Of Social Knowledge Construction In Online Discussionmentioning

confidence: 99%

“…A second contribution of this study is the evaluation of how the combination of external facilitation and role assignments affected the relationship between two different measures of the depth and quality of student participation, where positive changes in one measure were not always reflected in another measure. The use of two complementary perspectives in future studies is likely to become increasingly feasible, thanks to the development of automated classifiers for both CoI [9,11,21,23,25] and ICAP [1,20,36]. Combining the analytic approach presented here with real-time automated labelling could allow researchers and practitioners to benefit from rich analytic insights and to evaluate interventions while a course is still in progress.…”

Section: Discussionmentioning

confidence: 99%

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A network analytic approach to integrating multiple quality measures for asynchronous online discussions

Farrow

Moore

Gašević

2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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Asynchronous online discussions within a community of learners can improve learning outcomes through social knowledge construction, but the depth and quality of student contributions often varies widely. Approaches to assessing critical discourse typically use content analysis to identify indicators that correspond to framework constructs, that in turn serve as measures of depth and quality. Often only a single construct is addressed for performing content analysis in the literature, although recent work has used both social presence and cognitive presence constructs from the Community of Inquiry (CoI) framework. Nevertheless, there is no effective, commonly used, analytic approach to combining insights from multiple perspectives about quality and depth of online discussions. This paper addresses the gap by proposing the combined use of cognitive engagement (the ICAP framework) and cognitive presence (CoI); and by proposing a network analytic approach that quantifies the associations between the two frameworks and measures the moderation effects of two instructional interventions on those associations. The present study found that these associations were moderated by one intervention but not the other; and that messages labelled with the most common phase of cognitive presence could be usefully assigned to smaller meaningful subgroups by also considering the mode of cognitive engagement. CCS CONCEPTS• Computing methodologies → Modeling methodologies; • Applied computing → Collaborative learning.

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Section: Background 21 Analysis Of Social Knowledge Construction In Online Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A network analytic approach to integrating multiple quality measures for asynchronous online discussions

Farrow

Moore

Gašević

2021

LAK21: 11th International Learning Analytics and Knowledge Conference

View full text Add to dashboard Cite

show abstract

“…These data contain rich and multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the varying trend of the group and individual cognition processing. On the other hand, to analyze the cognitive levels in interactive discussion data, Garrison et al (2001) also provide an efficient instrument based on the CoI (Community of Inquiry) framework, which has been very widely used to analyze discussions data, especially in MOOCs (Hu et al, 2020). Furthermore, discussion pacings (i.e., instructor-paced or learner-paced) in a MOOC forum typically have a key effect on learning performance and cognitive processing (Moore et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners’ cognitive presence and learning achievements

et al. 2022

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The MOOCs (Massive Open Online Courses) forum carries rich discussion data that contains multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the development trend of the group and individual cognitive presence in discourse interaction. This paper describes a study conducted in the context of an introductory astronomy course on the Chinese MOOCs platform, examining the relationship between discussion pacings (i.e., instructor-paced or learner-paced discussion), cognitive presence, and learning achievements. Using content analysis, lag sequential analysis, logistic regression, and grouped regression approaches, the study analysed the online discussion data collected from the Astronomy Talk course involving 2603 participants who contributed 24,018 posts. The findings of the study demonstrated the significant cognitive sequential patterns, and revealed the significant differences in the distribution of cognitive presence with different discussion pacings and learning achievement groups, respectively. Moreover, we found that the high-achieving learners were mostly in the exploration, integration, and resolution phase, and learner-paced discussion had a greater moderating effect on the relationship between cognitive presence and learning achievements. Based on the findings and discussion, suggestions for improving the learners' cognitive presence and learning achievements in the MOOC environment are discussed.

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“…Moreover, due to the increasing amount of textual data contained in MOOC forums, manually coding each comment message was beyond the scope of this study. Hence, an automatic cognitive level encoder should be developed in future research utilizing robust machine learning approaches (Hu et al, 2020;Kovanovic et al, 2016). In addition, this study examined the cognitive processing and social interactions of learners with different achievements; however, it did not explore the factors that influenced cognitive and social characteristics.…”

Section: Conclusion Limitation and Future Workmentioning

confidence: 99%

“…Coding frameworks are commonly used to quantitatively classify textual content (Garrison et al, 1999;Gunawardena et al, 1997;Tsai, 1999). To analyze massive data of MOOCs, many researchers have developed automatic classifiers to reduce the cost of manpower and obtain ideal results (Hu et al, 2020; R. L. Moore et al, 2019). As an operationalization of the learning science theory of epistemic frames, epistemic network analysis (ENA) can analyze the cognitive data structure of connections and patterns (Shaffer, 2006).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum

Zhang

Peng

et al. 2021

Journal of Educational Computing Research

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In the field of learning analytics, mining the regularities of social interaction and cognitive processing have drawn increasing attention. Nevertheless, in MOOCs, there is a lack of investigations on the combination of social and cognitive behavioral patterns. To fill in this gap, this study aimed to uncover the relationship between social interaction, cognitive processing, and learning achievements in a MOOC discussion forum. Specifically, we collected the 3925 participants’ forum data throughout 16 weeks. Social network analysis and epistemic network analysis were jointly adopted to investigate differences in social interaction, cognitive processing between two achievement groups, and the differences in cognitive processing networks between two types of communities. Finally, moderation analysis was employed to examine the moderating effect of community types between cognitive processing and learning achievements. Results indicated that: (1) the high- and low-achieving groups presented significant differences in terms of degree, betweenness, and eigenvector centrality; (2) the stronger cognitive connections were found within the high-achieving group and the instructor-led community; (3) the cognitive processing indicators including insight, discrepancy, and tentative were significantly negative predictors of learning achievements, whereas inhibition and exclusive were significantly positive predictors; (4) the community type moderated the relationship between cognitive processing and learning achievements.

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Towards automated analysis of cognitive presence in MOOC discussions

Cited by 16 publications

References 18 publications

A network analytic approach to integrating multiple quality measures for asynchronous online discussions

A network analytic approach to integrating multiple quality measures for asynchronous online discussions

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners’ cognitive presence and learning achievements

Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum

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