Validation of the individual and collective self-efficacy scale for teaching writing in post-secondary faculty
Bibliographic record
Abstract
Faculty actions in the classroom are known to impact student writing self-efficacy and academic achievement. The purpose of this paper was to validate Locke and Johnston’s Individual and Collective Self-Efficacy for Teaching Writing Scales, a tool originally validated in high school teachers, in a new population of post-secondary faculty. Exploratory and confirmatory factor analysis methods were used in two studies with independent samples of multidisciplinary faculty (N = 281) for the exploratory factor analysis (Study 1) and nursing discipline specific faculty (N = 187) for the confirmatory factor analysis (Study 2). Three factors were identified in the questionnaire which maintained the essence of the theoretical structure proposed by Locke and Johnston. Factor 1 was named Context and Process Competencies, Factor 2 Textural Competencies, and Factor 3 Motivational Competencies. This factor structure was confirmed with acceptable goodness of fit in the confirmatory factor analysis Study 2. Learning to be a teacher of writing is a developmental process and this measurement tool has important validation information that speaks to its usefulness in understanding that process. • Instructional practices are known to impact student achievement levels. • Faculty individual self-efficacy for teaching writing is three factors. • Faculty undergo a slow enculturation practice to teaching writing. • This scale can be used to assess impact of teacher agency on student outcomes.
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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 | 0.018 | 0.037 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".