‘Learning from what my Mentor Teachers were Doing in the Classroom to Include Diverse learners’: Experiences that Contribute to the Use of Inclusive Instruction in Pre-Service Teachers
Bibliographic record
Abstract
All learners benefit when schools foster an environment for teachers to enhance their teaching skills. Pre-service education programs are often where this important process begins. Using a Global Concept Mapping method, pre-service teachers across Canada were interviewed about the personal and professional experiences that contributed to their instructional practices within inclusive classrooms. Participants sorted 93 unique statements into categories. They were asked to rate on a scale of 1 (not at all important) to 6 (very important) how important each experience would be to their instructional practices. Mentoring relationship statements were rated significantly more important than the others. Practicum experiences and those within the course part of the education program were rated as equally important and more important than professional development. Their perceived least important experiences were those related to past jobs/positions and personal experiences with people identified with diverse learning needs. Understanding the specific experiences that influence pre-service teachers’ perspectives of their inclusive instructional practices will help teacher education programs connect to what motivates teachers’ early experiences in becoming inclusive educators.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".