Digital Competencies and Professional attitudes as Predictors of Universities academics' Digital Technologies Usage: Example of Al-Hussein Bin Talal
Bibliographic record
Abstract
Information Communication Technologies (ICT) has experienced remarkable development and changes provoked by the spread of digitization and the rise of electronic technologies. Those changes made it urgent to understand academics' perceptions and professional usage of those technologies in higher education. To understand the academics' perception of digital technologies in higher education we have conducted this study in Al-Hussein Bin Talal University (AHU) as an example of academics' digital competencies, professional attitudes, and professional application of digital tools, and possibility of predicting the degree of application of digital tools through the degree of academics' competencies and professional attitudes . This study carried out in Ma'an, a poor-environment area of Southern Governorate in Jordan, with 107 academics who work in AHU as an instructors, has one aim which is to explore how they perceive new digital technologies. Most important result of the study showed that academics competence, attitudes, and digital technologies tools usage are in average level. Moreover the study showed that digital technology tool usage's degree can be predicted through the degrees of the academics' competence and attitudes.. Recommendations were included in this study.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".