Four distinct LMS interaction patterns with different final marks were found among 140 undergraduates in an authentic Moodle course.¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Cluster analysis of Moodle logs from 140 undergraduates identified four distinct behavioral patterns of LMS use, and these patterns were associated with different final marks. → Cerezo 2016
Evidence¶
Cerezo 2016¶
Cerezo, R., Sanchez-Santillan, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students' LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education. https://doi.org/10.1016/j.compedu.2016.02.006
q2 · i? · associational · r1
Observational log analysis of a Moodle 2.0 course with 140 undergraduates; the article reports that "four different patterns of learning with different final marks were found" using EM, k-means and the elbow method.
"After analyzing the log data gathered from a Moodle 2.0 course in which 140 undergraduate students were enrolled, four different patterns of learning with different final marks were found."
Discussion¶
Related Claims¶
- K-means clustering of LMS log data identified two distinct SRL profiles (high and low) among online students — 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
- K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles — related
- Semester control-question results correlated positively (r = 0.5530) with final-test results, supporting the evaluation system's predictive accuracy — related
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