A cluster of highly active learners driving the learning communities included equally many low-hierarchy as high-hierarchy members¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Beyond three clusters matching hierarchy levels, a cluster analysis identified a fourth group of the most active participants, who authored the most and highest-quality contributions and drove the communities' activities; half came from low hierarchy positions. → Cress 2015

Evidence¶

Cress 2015¶

Cress, U., Stahl, G., Ludvigsen, S., & Law, N. (2015). The core features of CSCL: Social situation, collaborative knowledge processes and their design. International Journal of Computer-Supported Collaborative Learning, 10, 109-116. https://doi.org/10.1007/s11412-015-9214-2

q2 · i? · review · r2

The editors describe the surprising cluster-analysis result of the Rhem et al. study: a fourth group of "the most active participants" acted as drivers and leaders of the learning communities despite including low-status members.

"They authored the most contributions and those with the highest quality. Half of the members in this group were from a high and the other half from a low position in the hierarchy."

Discussion¶

Related Claims¶

• Exploratory K-means clustering identifies four heterogeneous student AI-user profiles differing in experience, competence, usefulness, trust, and control, but the solution's optimality and stability are not established — related
• Teachers' post-workshop platform engagement fell into three behavioral profiles, with most teachers showing minimal creation and browsing — related
• Mastery in a quasi-community is dynamically and relatively distributed rather than structured as stable core-periphery hierarchy — related