ISTE Standards Implementation in Higher Education: An Exploratory Study of Prince Sattam bin Abdulaziz University, Saudi Arabia
Bibliographic record
Abstract
This study aims to explore the extent of utilization of the ISTE standards for students in higher education. It also aims to examine the impact of some demographic variables (gender, specialization, and academic degrees) on implementing the ISTE standards for the students. The researcher used a questionnaire method to collect the data from the students of Prince Sattam bin Abdulaziz University and sent it to the 626 participants who consented affirmatively and included 614 valid responses from them. A correlational analysis was carried out, and the statistical findings highlight that most of the participants signaled to achieve ISTE standards positively, and the availability of the standards was positive. The findings revealed statistically significant differences in responses between male and female students in all standards, with females outperforming males. Female participants exhibited a higher orientation in the use and implementation of the standards. However, the students' specializations and their respective degree programs made no significant difference in implementing the standards. The research overall finds efficacy in implementing the ISTE standards in all the universities for both the students and the teachers. It is recommended to embed these standards in the regular curriculum of the university's degree programs and create a regular mechanism to enhance awareness and provide training to the users to ensure efficacy in improving the educational standards.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".