WhatsApp Activities for Enhancing TEFL Pedagogical Knowledge and Classroom Practices: Suggested Types and Student-Teachers' Reflection
Bibliographic record
Abstract
This research was conducted to suggest a set of WhatsApp activities to enhance pedagogical knowledge and classroom practices that can be used in TEFL courses and to explore student-teachers' reflection towards the use of WhatsApp and the suggested activities. By reviewing related literature of using social networks and WhatsApp and though interviewing (9) TEFL instructors and (17) TEFL student-teachers, the researcher was able to suggest several activities that were used effectively in TEFL courses. These activities are: (1) reading materials (2) prediction ideas to get interest for the next lecture (3) videos (4) questions for flip classes or reviewing questions or proposing a problem to solve (5) open discussion topics or reflection on the lecture. A set of bases for using WhatsApp activities such as: posting clear content and having clear instructions for doing the activity, meeting FL student-teachers' language level or little higher language level, and not overloading TEFL student-teachers was presented. A group of (104) TEFL student-teachers from the faculty of education at Al-Azhar University-Gaza completed the following three reflection questions: what are the benefits of using WhatsApp and the suggested activities? What are the disadvantages of using WhatsApp and the suggested activities? What are the recommendations for improving the usage of WhatsApp applications and the suggested activities? Their responses were analysed qualitatively and quantitatively. The most common benefits were classified under (a.) pedagogical knowledge (b.) classroom practices (c.) review and evaluation (quizzes or tests) (d.) course requirements. On the other hand, the mentioned disadvantages were classified as (a.) technical and security problems (b.) communication problems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".