Advancing Pedagogical Alignment in a Bhutanese Teacher Education College: Employing an Action Research Approach Anchored in Bloom's Taxonomy
Bibliographic record
Abstract
Tutors in higher education institutions often face the challenge of aligning learning outcomes, subject matters, instructional activities, and assessment practices. Drawing from a decade of teaching experience, the authors employ practical action research (PAR) to develop a common anchor for achieving alignment. Their project unfolds in three phases: baseline data collection, intervention development and use, and post-intervention evaluation. Baseline data analysis revealed the intricacies of evaluating alignment within class lessons, emphasizing the need to reevaluate current practices. The interventions, mapped to the cognitive and knowledge dimensions of Bloom’s Taxonomy of Educational Objectives, were transformative tools aimed at achieving alignment. Post-intervention data analysis demonstrated tangible changes in lesson outlines and responses to follow-up questions, validating the effectiveness of the interventions. The authors underscore the importance of Bloom’s Taxonomy of Educational Objectives as a facilitator for alignment, offering tutors a practical and straightforward approach. The authors conclude by proposing the scalability of this approach during semester planning, providing tutors with a systematic framework to achieve alignment. Additionally, they suggest avenues for future research, exploring beyond the cognitive and knowledge dimensions to enhance the alignment phenomenon in educational practices.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".