Performance, Feedback, and Revision: Metacognitive Approaches to Undergraduate Essay Writing
Bibliographic record
Abstract
Abstract This paper explores ways in which frequent feedback and clear assessment criteria can improve students’ essay writing performance in a first-year English literature course. Students (n = 68) completed a series of three scaffolded exercises over the course of a semester, where they evaluated undergraduate essays using a predetermined assessment process. They were then asked to write their own essays and evaluate them using the same assessment criteria. The efficacy of the project was evaluated based upon student feedback, both quantitative and qualitative, and an analysis of their marks. The essay-writing project was informed by fundamental principles supported by research in teaching and learning: namely, that early intervention in first-year courses helps students improve their essay-writing skills, clear and transparent expectations are crucial for positive student perceptions around learning, carefully scaffolded assignment help students develop their writing skills over time, and increasing the frequency of writing opportunities and feedback leads to higher learning outcomes. Findings suggest that a metacognitive approach to essay writing can provide significant opportunities for students to improve their essay-writing skills. The essay-writing project has implications for those who plan, support, and deliver first-year university courses, particularly those courses involving academic writing assignments.
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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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".