Teachers’ collective efficacy with regard to inclusive practices—characteristics of a new scale and analyses from Canada, Germany and Switzerland
Bibliographic record
Abstract
Introduction While teachers’ individual and collective efficacy has been extensively studied with regard to promoting students’ academic success, teachers’ collective efficacy regarding inclusive practices has been largely neglected thus far, especially from an international perspective. International comparisons are of particular interest to any country or school system, respectively, as they can help to identify alternative approaches and opportunities for inclusive school development. The scale examined in this paper is ascertaining teachers’ collective efficacy with regard to inclusive education (TEIP-C) and is derived from a scale measuring (individual) Teachers’ Efficacy for Inclusive Practices (TEIP). This scale comprises three subscales termed Inclusive Instruction, Managing Behavior and Collaboration. Our major aim was to validate the tripartite structure of the original TEIP scale for the new TEIP-C scale and to demonstrate measurement invariance of the latter employing an international sample. Methods The sample comprised 897 teachers from Canada, Germany and Switzerland. Different Confirmatory Factor Analysis (CFA) models were combined with Exploratory Structural Equation Models (ESEM). Measurement invariance across countries was examined by means of a multiple group confirmatory factor analysis (MGCFA) approach. Afterwards, the variables gender, age and teaching experience were included simultaneously as predictors of collective teaching efficacy to specify a multiple indicator multiple cause model (MIMIC). Results We successfully validated the tripartite structure of the original TEIP scale for the new TEIP-C scale and demonstrated its measurement invariance employing samples from Canada, Germany, and Switzerland. Based on similar validations, it now appears possible for researchers to freely combine either of the six subscales focusing on teachers’ individual or collective efficacy with regard to inclusive education in their questionnaires in future studies. While the three country samples did not differ regarding Inclusive Instructions, significant differences in favor of Canadian teachers became apparent for Collaborations (compared to both, Switzerland and Germany) as well as Managing Behavior (Germany). Discussion Overall, the results underline the comparably high standards of inclusive teaching in Canada. Additional differences on the basis of the two subscales just mentioned pointed to somewhat lower ratings of collective teacher efficacy with respect to inclusive education by female teachers in Canada and Germany and older teachers in Switzerland.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".