Exploring the Effects of Mobile-Based Audience Response System on EFL Students’ Learning and Engagement in a Fully Synchronous Online Course
Bibliographic record
Abstract
Innovative technologies, such as the Audience Response System (ARS) provide an opportunity to steer students into active engagement and meaningful discussions. Many previous studies on the use of ARS, mainly in large traditional classes, have accentuated the positive impact in terms of increased students’ learning and engagement through the incorporation of ARS into classroom practices. However, in synchronous online courses, wherein the lack of visual contact tends to stifle active engagement, the impact of using ARS is certainly worthy of investigation. Thus, in this mixed method study, online English as a Foreign Language (EFL) students’ perceptions concerning the use of mobile-based ARS (M-ARS) and its impact on enhancing their engagement, interactivity, and learning attainment were examined via a questionnaire whereas challenges pertaining to the use of M-ARS were solicited via semi-structured interviews. The results revealed that the implementation of M-ARS in online teaching correlate significantly with EFL students’ engagement and learning experience whereas qualitative analysis revealed some important points with regard to integrating M-ARS into online classrooms. Directions and suggestions for future research are offered.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".