Interactive Learning Landscapes: Leveraging Technology for Dynamic Education in the Writing Classroom
Bibliographic record
Abstract
This study explores the perceptions of EFL learners towards interactive learning tools in the Saudi environment; it also investigates how available technology can be leveraged to create dynamic learning environments. Using a mixed methods approach, the study conducted a survey with 70 EFL learners and focus group interviews with 10 students across two higher education institutions to examine writing skill development using the three interactive writing tools, viz., Blackboard, Pear Deck, and Flipgrid. Results indicate that Saudi EFL learners in higher education have a moderately positive perception towards the integration of technology in the EFL classrooms, as well as support the efficacy of these interactive tools in aiding learning in the writing classrooms. Moreover, perceptional change evidently positively impacted learners’ writing as the performance in the post test showed an average improvement of between 19-24% in the scores This examination of the interaction between learners and digital resources is likely to uncover insights into effective strategies for enhancing English language learning experiences and outcomes, and benefit a range of stakeholders.
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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 | 0.004 |
| 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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".