Revising the material based on log files, mapped learning habits and evaluation results raised achievement in the following semester¶
Claim · All claims
Evidence · 1 study · causalr2·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? After revision (practical exercises moved into theory parts, image submissions for feedback, added examples and animations), new averages exceeded previous results in all four reported cells, e.g. regular-course theory 87.14% vs 80.95%. → Király 2016
Evidence¶
Király 2016¶
Sándor Király. (2016). How to Implement an E-learning Curriculum to Streamline Teaching Digital Image Processing. Acta Didactica Napocensia, Volume 9, Number 2. https://portal.issn.org/resource/ISSN/2065-1430
q2 · i? · causal · r2
Post-revision examination of a daytime group and a correspondence group in the next semester (Table 3): regular theory 87.14% vs previous 80.95%, regular practical 83.18% vs 68.24%, correspondence theory 89.55% vs 86.10%, correspondence practical 77.00% vs 57.37%. Only one student did not take the final test, so dropout remained very low.
"The results also showed that the material with revision ba sed on the examination of the log file s, the mapped learning habits, and the analysis of the results of the evaluation system, became more efficient in contrast to the earlier one."
Discussion¶
Related Claims¶
- Semester control-question results correlated positively (r = 0.5530) with final-test results, supporting the evaluation system's predictive accuracy — possibly the same claim (merge candidate)
- Regular-course students taught with the e-learning material scored more than 11 percentage points higher than control students on the same final test — related
- Log-file analysis links the greatest post-Opener gains to revisiting an evidence page and spending more time revising initial work — related
- Instructor co-designed tagging transforms raw LMS log data into structured event data reflecting course design and temporal context — related
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by process:wiki-ingest on 2026-11-02 |
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