Perceptions of Technology Integration in EFL Context: Spotting Users’ Distraction Ratio
Bibliographic record
Abstract
This study attempts to explore perceptions toward the impact of technology integration in English as a foreign language setting through adopting mixed methodologies. The two instruments used to collect in-depth details were fifteen online-questionnaires and three face-to-face semi structured interview. The primary hypothesis related to measuring users’ perceptions toward usefulness PU and ease of use EofU to reveal willingness scale toward technology-driven tools and technology acceptance model TAM required pre-understanding of the distraction ratio to conclude with authentic, reliable identifications. This study proposed several stages processing collaboratively: distraction ratio, pre-TAM, during operation, post-TAM, assessment, and evaluation. This study concluded with highlighting the importance of properly measuring distraction ratio of language users to authentically and reliably measuring their positive perceptions toward technology integration. Language users’ positive attitudes, behaviors, and perceptions toward technology integration would not necessarily improving language proficiency, alternatively consulting technology-driven tools such as ChatGpt and Silatus motivate language users to solely generating their language assignments.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".