Mobile Technologies and Knowledge Management in Higher Education Institutions: Students’ and Educators’ Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile devices are increasingly included for knowledge management (KM) in academic contexts. The purpose of this study was to examine how the integration of mobile technologies affects KM among students and educators in higher education settings in Saudi Arabia. Interviews with educators and students at two universities explored the factors determining the use of mobile technologies for learning. Content analysis of the participants’ responses found that the students and educators perceived four key factors determining mobile technology use: the capacity of mobile technologies to enhance learning processes, teaching practices, and student-student and student-educator communicative interactions, and hardware and infrastructure components. The main conclusion was that Saudi universities must utilise mobile technologies to identify, encapsulate, transform, and disseminate usable knowledge effectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it