An Exploration of Writing Self-Efficacy and Writing Self-Regulatory Behaviours in Undergraduate Writing
Bibliographic record
Abstract
Students will take independent action to improve their writing when they believe those actions will have a positive effect. The data presented focuses on the self-regulatory writing behaviours of nursing students in their third year. The purpose was to explore patterns of writing self-efficacy, anxiety levels, and student grade point average (GPA) in relation to student choices with help seeking, advanced planning of writing, revision habits, and response to feedback. Low writing self-efficacy, high anxiety students sought help from more sources, reported their feedback made them feel negative about their capabilities as writers, and were less likely to report reading and applying feedback to future writing efforts. No patterns of writing self-efficacy or anxiety levels emerged with respect to student revision habits or their choice to begin their assignments in advance of the due date. GPA was also not associated with the writing self-regulatory choices assessed. As the primary writing support for students in the later years of a nursing program, educators should consider interventions that encourage help seeking, facilitate students’ understanding and integration of the feedback they receive into their assignment revisions, and normalize the negative emotions that interfere with the self-efficacy levels required to write well.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".