Writing Self-Efficacy in Postsecondary Students: a Scoping Review
Bibliographic record
Abstract
Abstract In this review, we examine studies of writing self-efficacy conducted with postsecondary students published between 1984 and 2021. We aimed to inventory the methodological choices, writing contexts, and types of pedagogies explored in studies of writing self-efficacy with postsecondary students, and summarize the practical implications noted across the included studies. A total of 50 studies met eligibility criteria. All studies used quantitative methods, were conducted in English language settings, focused on undergraduate or graduate students, and included at least one writing self-efficacy measure. Across the 50 studies, the two variables most commonly appearing alongside writing self-efficacy were writing performance and writing apprehension. Many studies also assessed change in writing self-efficacy over time. Writing contexts and measures of writing self-efficacy varied across the included studies. Common practical implications noted across studies included students’ tendency to overinflate their writing self-efficacy, recognition of the developmental nature of writing ability, the importance of teacher attitudes and instructional climate, the influence of feedback on writing self-efficacy, and approaches to teaching and guiding writing. Based on this review, we see several directions for future research including a need for longitudinal studies, consideration of situated approaches, identification of diversity impacts, and attention to consistent use of strong multidimensional measures of writing self-efficacy.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".