Training Kuwaiti Teachers to Lead: A Case Study of Competitive Debate at the Basic Education College
Bibliographic record
Abstract
This paper presents a case study of a competitive debate program designed for teachers-in-training at the Basic Education College in Kuwait. Stakeholders at different levels have expressed an interest in introducing more constructivist-based pedagogies into the Kuwaiti national education system, but institutional and ideological challenges have hindered implementation. Teachers at the college designed and implemented a debate program based on constructivist principles of authenticity, student meaning-making, collaboration, and high performance expectations. Survey data suggest that participants experienced debate as a transformative experience, changing their perception of themselves, of the world, and of their ability to effect change in it. Participants came to imagine themselves as future system leaders preparing future generations with higher-order skills involving complex solving, which an increasingly complex social reality demanded. From 2015 to 2018, a group of professors formed debate teams at the Kuwait University National English Debate League. This endeavour formed the empirical research presented here as evidence to support a move from instructivist teaching to constructivist learning for future teachers in Kuwait.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".