THE EFFECT OF STUDENT REFLECTION QUALITY ON A TECHNICAL WRITING ASSIGNMENT RESUBMISSION
Bibliographic record
Abstract
The role of reflection and self-regulation in academic performance was tested using the "Exam Wrapper" strategy with a writing assignment for a technical elective course. The technical writing assignment involved the creation of a detailed outline for a technical report. This outline was submitted for grading and feedback before a subsequent extended technical report assignment. The outline was graded by the course teaching assistant, following a detailed grading rubric. After receiving the grade and feedback, students could resubmit a revised outline for re-grading, and include a reflection on the circumstances of their performance. Using the grading rubric, the resubmission was graded by the course instructor. A second graduate student evaluated the student reflection quality, and the resubmission quality. The effect of the self-reflection quality on re-submitted assignment improvement was assessed. The average grade improvement for students who resubmitted a reflection was +15.1 % (n=16), and for students who resubmitted without a reflection was +6.3 % (n=3). The difference between the average resubmitted and first submission grades positively correlated with reflection quality. These results suggest that a reflection exercise associated with a resubmission has potential to improve student technical writing quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".