The imperative for real-world experiences in Kenyan teacher preparation for disability inclusive teaching
Bibliographic record
Abstract
Traditional university-based teacher training in Kenya has relied on theoretical campus-based coursework which provides strong knowledge but little opportunity to develop practical skills. The need for practical skills is particularly evident for teacher candidates who are expected to teach students with disabilities who attend class in regular education settings. This case study outlines a pilot class at Daystar University that incorporated video, field trips, and real-world assignments within an experiential learning model to determine the impact of real-world experiences on student attitudes towards individuals with disabilities and their families, knowledge of disabilities, and skills in identifying and using understanding of learner strengths and weaknesses to make instructional recommendations and incorporate those recommendations into the design of class-wide learning activities. Teacher candidates enrolled in the pilot class reported positive changes in attitude and understanding towards individuals with disabilities, increased learning, and had more confidence in their ability to work in inclusive classrooms. These results have implications for ongoing curriculum restructuring in teacher education in areas of the world where educator preparation lacks the necessary resources for implementing a series of fully supported field experiences leading to a full-time teaching practice opportunity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".