Using WhatsApp to Enhance Students’ Learning of English Language “Experience to Share”
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".