Exploring factors that influence beginning teachers’ self-efficacy to teach in diverse classrooms
Bibliographic record
Abstract
Teacher self-efficacy for teaching in diverse classrooms is an important factor in the successful implementation of inclusion. Quantitative examinations of teacher self-efficacy have found the construct to be correlated with both contextual and teacher-related factors. In-depth qualitative exploration into type, quality, and nature of experiences that shape teachers’ self-efficacy beliefs is scarce. This research aimed to qualitatively examine potential sources of teacher self-efficacy and generate an explanation for the complex growth pattern it follows during the early years of practice. Seventy-eight beginning teachers across Canada (i.e., graduating teacher candidates and new teachers who are in the first three years of their practice) participated in 139 semi-structured interviews conducted over four years to address questions regarding the factors and experiences that influence their self-efficacy or confidence to teach in diverse classrooms. Ten factors which either had a positive or negative connotation emerged from a qualitative content analysis of their interviews. The Positive-Negative Experiences Balance (PNEB) model was conceptualized to understand and represent how these ten factors interactively, simultaneously, and collectively influence the development of beginning teachers’ self-efficacy for inclusive practice in the initial years of their careers. Through a comparison of frequency counts of codes, it was noted that beginning teachers differentially relied on experiential factors to enhance their self-efficacy when they were graduating, or were in the first three years of their teaching. The results are discussed in light of the relevant extant research. Implications of these results for teacher education programs and school leadership are also shared.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| 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".