Impact of Smartphone Based Activities on EFL Students’ Engagement
Bibliographic record
Abstract
Teachers all over the world strive to keep their students engaged, and research has shown that task engagement can be elevated by utilising technology to complete classroom activities. Reasons suggested for this is that technology’s alignment with students’ interests, as well as the stimulatingly transformative effect that technology can have on activities. Due to current students’ preferences, authors now encourage incorporating mobile phones into the classroom, claiming that it will improve task engagement. However, this has not been empirically proven. Therefore, this mixed method quasi experimental study examined whether two groups completing identical activities, where one group using their smartphones, would have any difference in their engagement with the given activities. The results indicated that a statistically significant difference in the initiation times and distraction between experimental and control settings. Although no significant emotional difference was observed between the groups, the students themselves indicated a significant difference in their emotional attitude towards smartphone activities as compared to paper-based ones. The smartphone group managed to engage with activities, thereby overcoming many factors which affected the control groups’ engagement levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".