A Critical Analysis of Bloom’s Taxonomy in Teaching Creative and Critical Thinking Skills in Malaysia through English Literature
Bibliographic record
Abstract
Purpose: The study aims to (1) review the literature that analyses the relevance of Bloom’s Taxonomy of Educational Objectives in the teaching of creative and critical thinking among students in Malaysia, and (2) identify missing aspects in Bloom’s Taxonomy vis a vis the indigenous context, important to promote creative and critical thinking among students in Malaysia.Method: Multiple sources of information which (1) documents the objectives of English Literature curriculum in Malaysia, (2) outlines the nature of Bloom’s Taxonomy, (3) reports past research which addresses issues in the application of Bloom’s Taxonomy, and (4) reports research findings on the issues in teaching English Literature as a subject.Findings: The literature subject is an essential avenue for students’ learning, especially in developing creative and critical thinking. The English syllabus with augmented taxonomy should be based on holistic learning outcomes which contain three set of abilities- Rationale Thinking, Purposeful Thinking, and Effective Relation with Contexts.Significance: The study would shed light on the effectiveness of teaching creative and critical thinking through English Literature. The findings may help curriculum developers and teachers to explore the missing aspects in the Bloom’s Taxonomy vis a vis the indigenous context, hence lead to the development of informed way forward in designing effective pedagogical approach/es that nurture creative and critical thinking among students.
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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 | 0.061 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".