Promoting Higher-Order Thinking in Saudi EFL Textbooks: A Comparative Study
Bibliographic record
Abstract
This study employs Bloom’s revised taxonomy (BRT) as a framework to examine the extent to which three English textbook series used in Grade 7 classes in Saudi intermediate schools—Lift Off, Full Blast, and Super Goal—encourage critical thinking by requiring students to use higher-order thinking skills (HOTS). Two of the series, Lift Off and Full Blast, have been discontinued, whereas Super Goal is currently the officially adopted series and a central element of educational reform under Vision 2030. The Grade 7 textbooks were analyzed through content analysis, with all tasks classified as requiring either lower-order thinking skills (LOTS) or HOTS. The findings indicate that LOTS predominate across all three series, though to varying degrees. The greatest imbalance was found in Full Blast, in which only 26.6% of tasks involved HOTS; Lift Off followed at 35.5%. The most balanced design was evident in Super Goal, where HOTS were required for 38% of tasks, suggesting a purposeful integration of creative and critical thinking skills. However, evaluation tasks—crucial for cultivating critical judgment—remain underrepresented even in this series. These findings suggest that although progress has been made in Saudi textbook reform, further efforts are needed to ensure that instructional materials fully support the development of 21st-century competencies. The results have direct implications for curriculum developers, educators, and policymakers, underscoring the need for continuous teacher training, systematic textbook evaluation, and assessment reform to ensure that classroom practices align with the goals of Vision 2030.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".