The influence of using smart technologies for sustainable development in higher education institutions
Bibliographic record
Abstract
Promoting sustainability development in education is a global endeavor, aiming to foster the sharing of experiences and knowledge on sustainability development. To achieve that, educational institutions worldwide have increasingly embraced educational technology and integrated online learning components into their instructional methods. This research focuses on the pivotal role of students as influential catalysts for advancing sustainable development within higher education. Specifically, it investigates the extent of students' familiarity with sustainable development initiatives within higher education institutions in the UAE. To achieve this objective, the study introduces the Technology-Integration Framework for Education Sustainable Development (TIFESD), which serves as an evaluative tool for appraising students' awareness of technology-driven elements woven into the broader context of Education for Sustainable Development (ESD) within their respective universities. The research employs a quantitative methodology, encompassing the collection of 513 survey responses from students across nine universities in the UAE. This data analysis explores the potential relationship between the integration of technology and students' cognizance of factors that bolster sustainable development. The study's outcomes underscore students' profound awareness of a spectrum of technology-driven elements, including Green Campus initiatives, Smart Education strategies, Smart Campus facilities, and the influence of curriculum and course offerings—all of which collectively contribute to the advancement of sustainable development practices within higher education institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".