Using Bloom’s Taxonomy to Evaluate the Cognitive Levels of Master Class Textbook’s Questions
Bibliographic record
Abstract
This study aimed at evaluating the cognitive levels of the questions following the reading texts of Master Class textbook. A checklist based on Bloom’s Taxonomy was the instrument used to categorize the cognitive levels of these questions. The researchers used proper statistics to rank the cognitive levels of the comprehension questions. The results showed that the author of Master Class emphasized the cognitive level of Comprehension having 52% of the questions, which was much more than the expected frequency, while wrote only 3.7% and 6% of the questions on the cognitive levels of Knowledge and Application respectively. The frequency of questions on the cognitive levels of Evaluation and Analysis were much closer to the expected frequencies. The results indicated that about 40% of the textbook’s questions emphasized higher-order thinking skills, which goes with the requirements of the revised curriculum. Evaluating and choosing a good textbook that goes with the goals of the curriculum is recommended. Such a study would shed light upon the role of textbooks in developing cognitive skills among Arab 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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".