Pre-service teachers' concerns about diversity
Bibliographic record
Abstract
Purpose The purpose of this study was to investigate pre-service teachers' concerns about including diverse learners in their classrooms. The study identified which concerns they ranked highest and lowest and which types of diversity they were most concerned about. The study also compared results in relation to demographic variables of gender, year and major. Design/methodology/approach Data collection relied on the Concerns about Inclusive Education Scale administered online with 343 pre-service teachers enrolled in higher education in Thailand. Analysis aimed to identify what were the highest categories of concerns as well as any significant relationships between concerns and demographic variables of gender, year and major. Analysis also identified the types of diversity about which pre-service teachers were most concerned along with any significant relationships between types of diversity and gender, year and major. Findings Results revealed that pre-service teachers ranked lack of resources as their highest concern about teaching diverse learners. Analysis revealed a significant difference for gender with females (p = 0.014) having a significantly higher level of concern about lack of resources than males. Mental health disabilities along with physical and learning disabilities were ranked highest in terms of types of diversity about which they were most concerned. There were no statistically significant differences for demographics regarding type of diversity about which teachers were most concerned. Originality/value There is a lack of research related to higher education's role in preparing teachers to teach in contexts of diversity. This study goes beyond traditional definitions to include 12 types of diversity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".