The Reality and Difficulties of Employing ICT in Teaching from the Perspective of Math Teachers of Middle Stage in Riyadh
Bibliographic record
Abstract
The present study aims to identify the reality and difficulties of employing Information Communication Technology (ICT) in teaching from the perspective of female Mathematics teachers of middle stage in Riyadh, Saudi Arabia. The study sample consisted of (165) female Math teachers of middle stage in Riyadh. The tool utilized is a questionnaire; and in order to answer the study questions and verify the validity of its hypotheses, the author used frequencies, arithmetic means, standard deviations, ANOVA, and the Tukey Test. The results showed that the degrees of the availability of most of the domains of using devices and applications in teaching of Math were (often and sometimes). The results also showed that the availability of most of indicators of ICT employment in the teaching of Math for the middle stage was (often), the degree of the difficulties that limit the employment of ICT in the teaching of Math from the perspective of Math teachers was high and medium. The results also showed that there were no statistically significant differences (α ≤ 00.05) between the means of responses of the participants due to both scientific qualification and specialization. The results also showed statistically significant differences (α ≤ 00.05) between participants’ responses due to years of experience. In addition, there were no statistically significant differences (α ≤ 00.05) between the means of responses of the participants on each domain of the questionnaire due to years of experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".