Deploy diffusion-based centralities to monitor productive discussion and academic success in CSCL¶

Strategy · All strategies
Evidence · 5 claims (5 for) · 2 studies (1 associational, 1 theoretical), q2 · 0 of 2 report an effect size · 5 claims rest on one study

Description¶

The article recommends using diffusion-based centralities as practical learning-analytics indicators for educators monitoring CSCL forums: "Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students' productive discussions and academic success." The strategy rests on the centralities' validated links to uptake, coded interaction quality and grades, and their computational ease relative to sophisticated diffusion models.

Design Implications¶

Context¶

Requirements¶

• a post-reply network representation of forum interactions

Constraints¶

Target Learners¶

• students in collaborative online higher-education courses

Target Learning Goals¶

• monitoring productive discussion, uptake, and study success

Affordances¶

• Diffusion Centrality Uptake Framework

Claims¶

• Student roles discovered by clustering diffusion-based centralities were validated with coded data and relate meaningfully to academic performance [+W]
• Diffusion-based centralities show moderate-to-strong statistically significant positive correlations with the frequency of information sharing and argumentation utterances in CSCL [+W]
• In a correlation meta-analysis across 69 course offerings, diffusion-based centralities correlated with academic achievement with high combined coefficients and predictive intervals [+W]
• A worked network example shows diffusion-based centralities capturing uptake and argumentation more accurately than traditional centrality measures [+W]
• Diffusion-based centralities elucidate information exchange and uptake in large-scale collaboration, validated against achievement and qualitative analysis, according to the editors' account of Saqr and López-Pernas's study [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Saqr, M., & López-Pernas, S. (2021). Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-021-09356-4