Bibliographic record
Abstract
These two studies examined the effects of professional development (PD) in the Three-Block Model of Universal Design for Learning (TBM of UDL) (Katz, 2013) for in-service teachers of students from kindergarten to grade 12, on teachers’ practices, efficacy and concerns about inclusive teaching. Effective PD requires intensive training that is delivered by experienced teachers, supports collegial dialogue, connects theory and practice and facilitates implementation in classrooms (Yoon, Duncan, Lee, et al., 2007). Some research indicates teachers’ attitudes and efficacy are impacted by education (Desimone, 2009), while Guskey (2003) suggested that ‘flow through’ is important – teachers observe that their changed practice increases student achievement, and then ‘buy in’ to the training increases. Total sample in the two studies included 103 teachers. Training involved a 5-day programme: introduction to the model followed by collaborative time planning, observing and problem solving. Data were collected using scales of teacher self-efficacy and concerns (Sharma, Loreman and Forlin, 2012) and the TBM Teacher Self-Assessment scale (Katz, 2014). Open-ended questions included: ‘What would help you to further develop your Inclusive Instructional Practice?’; ‘Tell us about your experience with UDL’; ‘What were the outcomes for you, your students, colleagues and families?”; and ‘What were the challenges?’ There were no significant differences before and after the intervention in teachers’ total concern scores, F(2,21) = .398, P = .534, or in their efficacy scores, F(2,21) = .192, P = .666. There were significant differences in teachers’ perceptions of their use of inclusive instructional practices [F (1,5) = 5.726, P < .05, η = .342], which was corroborated by classroom observations and student outcomes (Katz, 2013, 2014). Qualitatively, in subsequent interviews teachers indicated that the model improved their practice and self-efficacy related to inclusive education, reduced their workload and improved job satisfaction (Katz, 2014). Teachers indicated that their practices of differentiation, groupings and inclusivity of students with significant disabilities changed. Treatment group teachers reported that students with significant disabilities ‘engage in the same academic tasks as their peers’, while Control group teachers reported that ‘students with significant disabilities are “modified” academically – i.e. have a separate academic programme from the rest of their class’. This is an important finding, as research has shown that teachers view inclusion of children with significant disabilities as especially challenging (Smith, 2000). Thus, while quantitative data suggested teachers' concerns had not yet changed, their practices had, and they reported greater efficacy in interviews. Although research with pre-service teachers has shown that education enhances self-efficacy for inclusive teaching (Loreman, Sharma and Forlin, 2013), in-service teachers appear to differ. Enhanced efficacy for inclusion was not developed with teachers’ initial education or implementation. In the case of these studies, the practice changes and elevated student achievement preceded changes to teachers’ beliefs in the forms of efficacy beliefs and concerns. This fits with Guskey's flow through concept (2003). Qualitative analysis appeared to confirm this, as responses included such comments as ‘UDL and all of the related concepts we explored have left me with confidence and direction as a teacher’, reflecting teachers’ increasing self-efficacy related to inclusive education. The author reports no conflict of interest.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".