Using WhatsApp to Enhance Students’ Learning of English Language “Experience to Share”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Education system has developed rapidly, technology has invaded our life, everyone has smart phone these days, using WhatsApp, Facebook, Twitter, Instagram, Telegram, etc. No one can deny that the generation we teach these days, has become addicted to these applications, for social relationship and fun. In 2012 when I started teaching in KSA, Blackboard has been newly introduced for teaching in King Khalid University, but most of the students were reluctant to use it due to net access problems and unfamiliarity. This study was conducted in College of Science & Arts Majarda King Khalid University, English Department. The population of the study were 36 female students from 1st level who were studying Listening & Speaking 1 course in the 1st semester 2013-2014. The researcher has used the analytical descriptive method to conduct this study in King Khalid University. A students’ questionnaire and instructor observation were the tools for collecting the data, results were coded manually and analyzed using SPSS. Almost all study-findings supported using WhatsApp to enhance students learning and enthusiasm, using WhatsApp helped students to develop English skills, enriched their vocabulary and learn from their mates mistakes, although the study laid out some disadvantages of the experience such as preparing the materials and having discipline in the group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it