Understanding the role of digital information in enhancing education in UAE: An investigation of the factors that drive continuous adoption
Bibliographic record
Abstract
Digital information has had a significant impact on higher education, transforming the way students learn and interact with course materials, professors, and their peers. Education systems that place a high priority on learning satisfaction and tutor quality frequently ignore the problem of limited access to digital information and technology. In this study, we provide an integrated model that examines how the TAM constructs of digital information in education (DIE) are affected by digital information flow, technological readiness, learning satisfaction, and tutor quality. We provide information on the results of a project evaluation that examined how digital information is used in higher education. We gathered information from a survey of 594 college students to validate our model and hypothesis. Our research suggests that external factors that improve users' technological readiness and learning satisfaction may have an impact on how valuable they perceive DIE to be. A user's traits, particularly their level of technological preparedness, have a significant impact on how simple technology is to use. The user's perceived usefulness of the technology may also be further enhanced in some cultures by the tutor’s perceived excellence. External elements like the flow of information may have a significant impact on the intention to use technology.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".