The Role of Experiential Learning in Teachers’ Professional Development for Enhanced Classroom Practices
Bibliographic record
Abstract
The main objective of this article was to explore how experiential learning theory can be adopted into teachers' professional development, as an effective approach to engaging teachers in classroom-enhanced activities. The inclusion or design of various experiential learning activities will promote pedagogical content knowledge and teaching skills that can enhance classroom practices. The theoretical framework of experiential learning enables effective classroom instructional delivery for all learning experiences in different contexts. The study purposefully selected 10 teachers, teaching Economics, from 10 high schools in Lagos, Nigeria. These teachers were observed in different classroom settings/teaching of selected topics from Economics, before and after a 3-day experiential learning-based professional development workshop. The teachers were further engaged in a focus group interview after the final observation. Findings revealed that experiential learning theory is critical to teacher professional development, as it enables teachers to learn better when engaged in experientially designed professional development, which can impact their classroom teaching thereafter. The study, therefore, encourages teacher educators or government bodies responsible for content design for teachers' professional development to integrate or structure the contents of teachers' professional development with experiential learning initiatives, to improve teachers' classroom pedagogies and teaching competencies for quality education.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".