The largest cluster of learners invests minimally in the platform and shows almost no gain in conceptual knowledge¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Cluster 1, the largest cohort with 226 learners, invested the minimum learning time with the platform, tended to use less text than other clusters, and showed almost no gain in conceptual knowledge. → Marvin Roski 2024

Evidence¶

Marvin Roski 2024¶

Marvin Roski, Ratan Sebastian, Ralph Ewerth, Anett Hoppe, Andreas Nehring. (2024). Learning analytics and the Universal Design for Learning (UDL): A clustering approach. Computers & Education 214 (2024) 105028. https://doi.org/10.1016/j.compedu.2024.105028

q2 · i? · associational · r1

Descriptive characterization of Cluster 1 (226 learners, 58.9% of the sample) from the log-file and pre/post-test data. The cluster's z-standardized characteristics sit around 0 with small error bars, and its members "show almost no gain in conceptual knowledge"; no effect size or test statistic is printed.

"The usage patterns show that the learners have invested the minimum learning time with the interaction of I3Learn. Compared to the other clusters, the learners of Cluster 1 tend to use less text. They show almost no gain in conceptual knowledge."

Discussion¶

Related Claims¶

• The six clusters differ substantially in usage duration of UDL-guided elements, with the small Cluster 2 investing particularly more time than Cluster 1 — related
• K-means clustering on EFA-derived features yields six learner groups with distinct usage patterns of UDL-guided elements — related
• A K-means clustering of students based on pre- and post-project SDG knowledge identifies three distinct learning profiles, with the largest cluster showing the greatest knowledge gain. — reports the opposite