The extent to which Arabic language teachers accept the use of social media to develop Arabic language skills, writing, reading, conversation, and listening to fourth grade students in Kuwait
Bibliographic record
Abstract
Due to the increasing spread of the social media use especially between the young generations, the effect of the social media on the learning process has been a matter of discussion especially the teachers' acceptance of such phenomenon. The aim of this study is to find the extent to which Arabic language teachers accept the use of social media to develop Arabic language skills, writing, reading, conversation, and listening to fourth grade students in Kuwait. This study used the descriptive analytical approach to answer the study questions. A questionnaire was distributed on the study sample consisted of (150) male and female teachers for the fourth grade students in Kuwait. The results of this study showed that the teachers were mostly accepted the reading and writing skills with a high degree, while they accepted the conservation and listening skills in a moderate level. The results showed also that there are significant differences in the Arabic language teachers' acceptance of the use of social media to develop Arabic language skills, writing, reading, conversation, and listening to fourth grade students in Kuwait attributed to the gender variable for the favor of the females, while there were no statistically significant differences attributed to the experience variable.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".