iPads for Cognitive Skills in EFL Primary Classrooms: A Case Study in Saudi Arabia
Bibliographic record
Abstract
This research study was designed to clarify the effectiveness of innovative technology use in order to develop cognitive skills in Saudi Arabia with particular focus on the use of iPads in English as a Foreign Language (EFL) classes. New technology approaches are continually being implemented in educational environments but there is often lagging analysis as to the effectiveness of these approaches. In the context under review the implementation of iPads represented a significant shift from using paper and pen to using a portable touchpad and digital pen. This qualitative study comprising observations, interviews and focus groups with teachers and students in four primary EFL primary classrooms in Saudi Arabia. It aimed to investigate any links between EFL teaching approaches, revised Bloom’s Taxonomy of thinking skills and the use of iPads. The findings indicated an unevenness in the application of revised Bloom’s Taxonomy in English instruction generally and most iPad teaching practices were represented at lower order thinking levels (Remember, Understand and Apply). Also, flexible use of iPads when teaching-learning EFL represented levels of revised Bloom’s Taxonomy which aligns with specific roles of; teacher (T), teacher-student shared role (TS) and student (S) and plays a part in representing cognitive skills. These findings contribute to tablet devices use in language learning literature by highlighting the ‘how’ of EFL instruction based on revised Bloom’s Taxonomy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".