Beginning Teachers’ Development of Inclusive Practices: A Longitudinal Multiple-Case Study Approach
Bibliographic record
Abstract
Classroom teachers are expected to have the competencies to teach in inclusive classrooms, with the goal of providing diverse learners the support and opportunities they deserve to learn at their best. However, teachers worldwide have reported their teacher education program was inadequate in preparing them to teach in inclusive classrooms. To address this, Sharma (2018) proposed the 3H framework – the three apprenticeships that preservice teachers should be prepared with to become effective inclusive educators: the heart (attitudes and beliefs of inclusive teachers), the head (knowledge and self-efficacy to teach diverse learners), and the hands (inclusive instructional practices). The goal of this study was to investigate the development of these three apprenticeships in teachers’ early career longitudinally. Eight Canadian beginning teachers were interviewed for four consecutive years about experiences that have contributed to their beliefs, self-efficacy, and instructional practices to teach in diverse classrooms, presented as case studies. Participants’ ideas were further examined collectively, resulting in nine themes: (a) “getting thrown in” to experiment with strategies; (b) gap between theory and reality – what worked or did not work in formal education; (c) what is inclusion?; (d) beliefs in children’s ability and exposure to diverse learners; (e) responsibilities as teachers and recognizing limits; (f) building relationships with students and caregivers; (g) a supportive and knowledgeable team; diversity and other components that affect learning; and (h) books, media, and technology. Developmental patterns were also noted. First, inclusive beliefs and instructional practices taught in teacher education programs were consistently mentioned across the four years of interviews but were not attributed to the influence of their education. Second, participants often began their career with confidence, but it decreased sharply as they experienced a “reality shock”; as time progressed, they either (a) accepted their limits as teachers and met students’ needs to the best of their abilities or (b) felt defeated and lost faith in the inclusive education system. The findings of this study added to our understanding of beginning teachers’ perception of where the issues lie, and directions for improving educational programs so they can better prepare preservice teachers for inclusive education.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".