Faculty Perceptions Pertaining to Attributes of Successful Universities and Future Learning Environments
Bibliographic record
Abstract
Significant changes are driving the wheels of progress. In the context of higher education, developments in technology and globalization have made a profound impact. There is need for universities to take stock of developments to plan with realistic goals so as not to be left behind in a highly competitive globalized environment. With rapid changes in requirements, universities face the challenge of being relevant. There is need to identify the attributes of successful universities and future learning environments for universities to prepare themselves towards achieving success. This study investigates the perception of respondents pertaining to attributes of successful universities and future learning environments. Data were collected using a specially designed survey during the 2016 academic year. The sample size was 89 international academics. The results highlights critical attributes, management elements and dominant pedagogical and technological trends. The paper also reports significant differences between gender and discipline sub-groups. The findings identify key themes, trends or perceptions that can be used as a foundation for more in-depth research to discern possible strategies towards achieving success.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".