The degree of using the smart board in providing students with planning skills to teach Arabic language and their attitudes towards it among the three stages students in Kuwait
Bibliographic record
Abstract
With the technological advances that have revolutionized the different fields, the educational processes have been influence too. Smart board is considered as one of the promising approaches to enhance the educational process as approved by many studies. The aim of the current study is to examine the degree of using the smart board in providing students with planning skills to teach Arabic language and their attitudes towards it among the three stages students in Kuwait. This study has used the descriptive analytical approach to fulfill the aims of the study. A validated questionnaire was distributed on (90) students from the three stages in Kuwaiti public schools where the means and standard deviations for their answers were calculated. The results of the current study revealed that the smart board is used in a high degree in providing students with planning skills to teach Arabic language. The results also showed that the students have a positive and good attitude toward using smart board in teaching Arabic language. This study recommends involving smart board in wider classroom management skills and applies such scales on different samples including administrators and teachers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".