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.¶

Claim · All claims
Evidence · 1 study · design r2 · q3
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Three clusters emerged: 'from very little I learn something' (n=74), 'from almost nothing I learn a lot' (n=183), and 'from the intermediate I learn something' (n=44), with uneven distribution across universities. → Blanco 2024

Evidence¶

Blanco 2024¶

Blanco, J. M., Domínguez, C., Jaime, A., & Usandizaga, I. (2024). Connecting sustainability and computer science curricula through website learning projects embedding different types of student-generated content. Education Tech Research Dev. https://doi.org/10.1007/s11423-024-10376-8

q3 · i? · design · r2

K-means clustering of 301 students using pre- and post-project SDG knowledge ratings. The study reports three clusters with the largest being "from almost nothing I learn a lot" (n=183). Cluster sizes and means are reported in Table 6. No effect sizes are printed.

"The result of this classification with a clearer interpretation had three clusters. The option with two and four clusters was discarded because the first one grouped two clusters quite differently and the second one produced two clusters with a very similar interpretation."

Discussion¶

Related Claims¶

• Students perceive content-generation activities (web article search, video generation, self-assessment question generation) as more effective for learning SDG content than activities focused on reviewing peer materials. — related
• 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
• Secondary students in AI learning fall into three distinct SDT-based motivational profiles — Disengaged, Developing, and Self-Determined — at both pre- and post-test — related
• Hierarchical clustering of CDM attainment probabilities identifies five learner groups with distinct patterns of professional standards attainment — related
• K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles — related
• Three distinct learner profiles emerge from function art activities: Repetitivists, Simplists, and Multifunctionists — related
• The effectiveness of the SGC methodology is consistent across different university contexts, with no significant differences in final SDG awareness, interest, or satisfaction between student groups. — related
• Completing a series of student-generated content projects significantly increases students' subjective knowledge of Sustainable Development Goals. — related