An Investigation into University English Language Instructors’ Inclusion of the Revised Bloom's Taxonomy of Cognitive Skills in Testing Language Skills: Selected Universities in Focus
Bibliographic record
Abstract
The purpose of this study was to investigate into university English language instructors’ inclusion of the revised Bloom's Taxonomy of Cognitive skills in testing undergraduate students’ language skills. The participants of the study were 32 English language instructors who were offering English language courses to undergraduate students. They were selected using a purposive sampling. All the participant-instructors were made to fill in a close-ended questionnaire and a semi-structured interview was held with four of them. Document analysis was also conducted using the judgments of the English language experts. Data collected by the questionnaire was analyzed quantitatively using frequency counts and percentages, whereas data gathered through the interview was analyzed qualitatively. The study employed a descriptive research design and mixed-methods approach. The study's findings revealed that the test items are hardly higher-level cognitive skills (analyzing, evaluating and creating); they are completely dominated by lower-level cognitions (remembering, understanding and applying). Hence, it is recommended that university ELT instructors should give due attention for the revised Bloom’s taxonomy of cognitive skills in testing their students’ language skills.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".