Think twice: exploring the effect of reflective practices with peer review on reflective writing and writing quality in computer-science education
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".