Experiential Learning, Conditional Knowledge and Professional Development at University of Nairobi, Kenya—Focusing on Preparedness for Teaching Practice
Bibliographic record
Abstract
Experiential learning requires teacher educators to equip trainee teachers with opportunities for effective preparedness in teaching and professional subjects, co-curricular activities and in micro-teaching vital for professional development. The experiential learning opportunities, conditional knowledge, preparedness and performance during teaching practice provide basis for predicting professional competence and success for effective teaching. Conditional knowledge entails application of critical thinking and problem solving skills that demonstrate mastery of theoretical knowledge and professional practice across, content, knowledge, skills and insights. This type of knowledge and skills are developed through experiential learning coupled with effective preparedness for real-class instructional management. However, inadequate preparation in educational courses coupled with improper supervision and feedback impede effective professional development in most universities. The study explored effectiveness of experiential learning and conditional knowledge in trainee teacher preparedness for teaching practice at the University of Nairobi. Experiential Learning Theory formed the framework for this study. A descriptive survey research design was adopted with a population of 78 trainee teachers selected using simple random sampling. Data were gathered through a questionnaire. Finding showed that trainee teachers are adequately prepared for teaching practice. The study recommends proper orientation for trainee teachers to be carried out with effective preparedness that aligns theory to practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".