Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

نویسندگان

چکیده

Abstract This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, consistent indicators success computer-supported collaborative learning. The analysis is based on a large dataset 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ coded representation their order to investigate extent are able adequately capture exchange processes. performed meta-analysis pool correlation coefficients measures academic achievement across all while considering sample size each course. Lastly, from cluster using aimed at discovering role-taking within interactions, we investigated validity discovered roles data. There was statistically significant positive that ranged moderate strong frequency sharing argumentation utterances, confirming important aspects uptake. results showed had highest most combined well predictive intervals, thus demonstrating advantage over traditional centrality measures. Characterizations diffusion validated qualitative methods found meaningfully relate performance. Diffusion-based feasible calculate, implement interpret, offering viable solution can be deployed any scale monitor productive discussions success.

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ژورنال

عنوان ژورنال: International Journal of Computer-supported Collaborative Learning

سال: 2021

ISSN: ['1556-1607', '1556-1615']

DOI: https://doi.org/10.1007/s11412-021-09356-4