IMPROVING STUDENT TEACHING FOR QUALITY TEACHER PREPARATION: A KENYAN UNIVERSITY CASE
Bibliographic record
Abstract
This study on teaching practice experience was conducted at a Kenyan University by researchers from both the USA and Kenya through a partnership project to build capacity through quality teacher preparation. The portion of this study presented here used survey techniques and specifically addressed the student teachers’ perspectives on the preparation processes, and ability to plan, instruct and use feedback to improve instruction in teaching practice. Stratified sampling of student teachers (n=360) and supervisor (n=240) was used. The student teacher questionnaire covered several educational components such, as professionalism, lesson material preparation, content knowledge, teaching performance skills, and reflection based on classroom observation feedback. The major findings were student teachers inability to integrate Information Communication Technology (ICT) in teaching, a gap in the teacher education curriculum on the role of ICT in teacher education, and lack of supportive supervisory feedback to the teacher candidates during teaching practice. The study recommends mapping of teacher education courses to ensure that ICT and expert feedback are covered before teaching practice by offering coursework on modern accessible ICT and facilitating rigorous microteaching experiences. Also, programs should train enough teaching practice supervisors, strategically plan school placements, and ensure timely posting of student teachers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".