Think twice: exploring the effect of reflective practices with peer review on reflective writing and writing quality in computer-science education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reflective writing is a proven way to increase the quality of learning and knowledge construction. However, its use in computer science education has received little attention. In this mixed-methods study, we investigated the effect of reflective writing practices, including peer review, on students’ reflective writing and writing quality scores in a computer science education context. Three reflective writing assignments were required in a Human Computer Interaction course and two peers reviewed each assignment to give feedback. Rubrics were used to measure the reflective writing and writing quality characteristics of student work, and a peer feedback coding scheme was used to determine the characteristics of the feedback students provided to one another. Results revealed that students’ reflective writing and writing quality did not differ across projects and they offered solutions as their most common type of feedback. Our results revealed further studies need to keep investigating new approaches in terms of timing, guidelines, and supportive tools to promote reflective writing to determine which activity designs facilitate student improvement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it