An Assignment Wrapper Promotes Student Self-Regulation of Learning in a Science Writing Assignment
Bibliographic record
Abstract
We investigated the impact of an assignment wrapper in promoting self-regulation of learning in a series of written assignments for a First-Year Biology laboratory. Students completed a planning survey prior to submitting an Introduction assignment. Upon receipt of the graded Introduction, students completed an assignment wrapper, two more written assignments, and an end-of-term reflective survey used to measure the impact of the assignment wrapper on students’ approaches to writing assignments. In the planning survey, 46% of students accurately described the assignment requirements. Many reported high levels of stress (85.3%–87.3%) and anxiety (54.0%–59.9%) while planning and preparing the assignment. In the assignment wrapper, 67.9% reported having spent more time than expected on the assignment and 98% students indicated that they would change their approach for the next assignment. Students reported in the reflective survey that both the planning survey and assignment wrapper helped them consider different strategies when completing writing assignments (e.g., asking for clarification, avoiding procrastination, becoming aware of emotions and managing them constructively). Important implications for instructors include: creating a culture of help seeking, providing timelines for stages of writing, and acknowledging that emotional struggles are common among First-year students in STEM courses.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".