Digital Teaching-Learning Technologies: Fostering Critical Thinking in Language Classrooms in Saudi Arabia
Bibliographic record
Abstract
This study investigates the wide variety of the current digital teaching-learning technologies that Saudi EFL teachers use to engage students' critical thinking skills. Furthermore, this research also explores the critical thinking skills that developed as a result of the use of technology in the Saudi context. Data were collected through a questionnaire to get teachers’ feedback and opinions about digital applications used and the critical thinking skills employed. Forty teachers from six English language institutes and four English departments in different cities of Saudi Arabia participated in this study. Results indicate that the use of technology tools /applications, and popular game and pool apps in teaching English including games and pools using Kahoot!, Quizziz, and Quizlet is highly favored in language classes. Moreover, following instructions and applying language rules are the priority critical skills targeted by language teachers when they use games and pools, and breakout groups using Blackboard, Zoom, Google Meet, Chat, Online Forum, and Instant Messaging are most employed by language teachers for collaboration and discussion purposes in their classes. This study also found that applying language rules, following instructions, brainstorming, determining facts and opinions, stating opinions and analyzing problems are the more frequently targeted critical thinking skills. It is recommended that teachers and trainers with an insight on how to harness and perhaps integrate these technological tools in their teaching-learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".