Smartphone Apps -based Teaching Method to Develop Oral English Communication Skills at the Tertiary Level in an EFL Context
Bibliographic record
Abstract
Smartphones are the most used hand-held devices by people globally, whereas large class size is a common scenario in many EFL (English as a Foreign Language) contexts like Bangladesh. Besides, EFL learners get scarce opportunities to practice oral skills inside and outside the class to develop oral English communication skills (OECSs). Thus, this study investigates learners' experiences after the intervention of 9 weeks with a smartphone apps-based teaching method (SBTM) employing WhatsApp, call, and voice recorders in an EFL classroom at the tertiary level for managing large-size class for developing learners' OECSs in Bangladesh. For this purpose, a qualitative research design using interviews, reflective journals, and classroom observation was used to elicit learners' experiences and practices for soliciting a model of a smartphone apps-based teaching method (SBTM). The findings showed that learners had positive experiences, e.g., ubiquitous and flexible processes, opportunities for individual and partner practice, recordings facilitated oral practice, and inside and outside classroom oral practice for managing large-size classes for developing OECSs in an EFL context. On the other hand, the negative experiences that learners reported were that this method was challenging for teachers, e.g., for assessment, and the classroom became noisy. The findings of this study will leave implications for teachers, learners, app developers, policymakers, and researchers for practising, developing a new app, and adopting a policy for implementing technology inside the classroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".