Effects of Proactive Personality and Social Centrality on Learning Performance in SPOCs

Liu, Sanya; Chai, Huanyou; Liu, Zhi; Pinkwart, Niels; Han, Xue; Hu, Tianhui

doi:10.5220/0007756604810487

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…This study replicates previous findings (Galikyan & Admiraal, 2019;Liu et al, 2019;Saqr & Alamro, 2019) that there are statistically positive associations between individual measures in the social network and learning performance. This supports several famous learning theories (eg, social constructivism, Vygotsky, 1978;community of inquiry, Garrison et al, 2001) proposing that social and cognitive interactions are two key elements of successful learning, which in turn is considered as a collaborative process through shared and joint activities.…”

Section: Discussionsupporting

confidence: 91%

Learners' interaction patterns in asynchronous online discussions: An integration of the social and cognitive interactions

Liu

Chai

et al. 2021

Brit J Educational Tech

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Studying the networked nature of social and cognitive aspects of learner interactions is the key to understanding how successful collaborative learning occurs in asynchronous online discussion forums (AODFs). Guided by network science and multiplex network analysis, this study compared the differences of network structure and properties between the social (learners as nodes and commenting on the others' contributions as edges) and cognitive (learners as nodes and explicitly quoting the others' contributions as edges) networks within an AODF. It additionally examined the differences of individual measures between these two monolayer networks and their integrated two multiplex networks. The two multiplex networks were respectively framed in superimposed and unfold approaches. Moreover, correlation analysis was conducted to examine the relationships between individual measures and learning performance across the four networks. Results showed key differences at the network and individual levels between the social and cognitive networks. The two networks had different compositions of participants, and students in the cognitive network occupied more central positions in general. Besides,

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Section: Discussionsupporting

confidence: 91%

Learners' interaction patterns in asynchronous online discussions: An integration of the social and cognitive interactions

Liu

Chai

et al. 2021

Brit J Educational Tech

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“…Regarding closeness centrality, our findings showed a moderate combined correlation coefficient but a non-statistically significant predictive interval, pointing to its poor reliability as an indicator of student success. These results are supported by the existing literature, which showed a negative statistically significant correlation in one study (Cadima et al, 2012), as well as contradictory results within the same study among different courses (Gašević et al, 2019;Jiang et al, 2014;Joksimović et al, 2016;Liu et al, 2019). Most of the contradictory results have been reported in MOOCs where that pattern has been observed before.…”

Section: Discussionsupporting

confidence: 72%

“…Degree centrality refers to the total sum of posts, messages, or interactions sent and received by an actor (Cadima et al, 2012;Cho et al, 2007;de-Marcos et al, 2016;Joksimović et al, 2016;Liu et al, 2019;Liu, Kang, Su et al, 2018;Liu, Kang, Domanska et al, 2018;Osatuyi & Passerini, 2016;Putnik et al, 2016;Reychav et al, 2018;Wise & Cui, 2018). The interpretation of degree centrality varies by context and task: in collaborative learning, student posts are expected to establish social presence, engage in knowledge coconstruction, or share resources.…”

Section: Degree Centralitiesmentioning

confidence: 99%

The Curious Case of Centrality Measures: A Large-Scale Empirical Investigation

Saqr

López-Pernas

2022

Learning Analytics

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There has been extensive research using centrality measures in educational settings. One of the most common lines of such research has tested network centrality measures as indicators of success. The increasing interest in centrality measures has been kindled by the proliferation of learning analytics. Previous works have been dominated by single-course case studies that have yielded inconclusive results regarding the consistency and suitability of centrality measures as indicators of academic achievement. Therefore, large-scale studies are needed to overcome the multiple limitations of existing research (limited datasets, selective and reporting bias, as well as limited statistical power). This study aims to empirically test and verify the role of centrality measures as indicators of success in collaborative learning. For this purpose, we attempted to reproduce the most commonly used centrality measures in the literature in all the courses of an institution over five years of education. The study included a large dataset (n=3,277) consisting of 69 course offerings, with similar pedagogical underpinnings, using meta-analysis as a method to pool the results of different courses. Our results show that degree and eigenvector centrality measures can be a consistent indicator of performance in collaborative settings. Betweenness and closeness centralities yielded uncertain predictive intervals and were less likely to replicate. Our results have shown moderate levels of heterogeneity, indicating some diversity of the results comparable to single laboratory replication studies.

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“…It measures the engagement level of the learner, the distance to all others in the discourse, and closeness to the collaborators. It is often operationalized as the ease of reachability and the ease of access to information (e.g., Hernández-García et al 2015;Liu et al 2019;Osatuyi and Passerini 2016). Betweenness centrality represents learner engagement in the discourse.…”

Section: Operationalization Of Centrality Measures In Csclmentioning

confidence: 99%

Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter?

Saqr

Viberg

Vartiainen

2020

Intern. J. Comput.-Support. Collab. Learn

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The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students' learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students' participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.

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Effects of Proactive Personality and Social Centrality on Learning Performance in SPOCs

Cited by 9 publications

References 20 publications

Learners' interaction patterns in asynchronous online discussions: An integration of the social and cognitive interactions

Learners' interaction patterns in asynchronous online discussions: An integration of the social and cognitive interactions

The Curious Case of Centrality Measures: A Large-Scale Empirical Investigation

Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter?

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