Distance Education for Traditional Universities: Part-Time Professional Learning
Bibliographic record
Abstract
Traditional universities are experiencing considerable pressure to make their courses and programs accessible at a distance. Unfortunately, senior administrators in traditional universities are skeptical and do not understand that there are distance education technologies and approaches that are consistent with the values and culture of their institutions. It is argued that the traditional research universities can accomplish several goals by focusing on the development of relevant continuing professional education programs that are accessible through distance education. Moreover, if the capabilities of collaborative synchronous and asynchronous communications technologies are used to their full potential, the integrity and quality of traditional research universities can be sustained. One important side benefit may be that traditional universities will rethink their approaches to teaching and learning for both on- and off-campus courses and programs. Les universités traditionnelles subissent des pressions considérables afin de rendre leurs cours et leurs programmes accessibles à distance. Malheureusement, les cadres supérieurs de ces universités sont sceptiques et ne comprennent pas qu’il existe des technologies et des méthodes d’éducation compatibles avec leurs valeurs et leur culture traditionnelles. Nous prétendons que les universités traditionnelles à vocation de recherche peuvent atteindre plusieurs de leurs objectifs en mettant l’accent sur l’élaboration de programmes d’enseignement professionnel continu qui seraient accessibles à distance. Qui plus est, si l’on exploite à fond le potentiel des technologies de communication, synchrone et asynchrone, pour la collaboration, l’intégrité et la qualité des universités traditionnelles à vocation de recherche pourront être maintenues. En outre, ces universités pourraient être amenées à repenser leur manière d’aborder l’enseignement et l’apprentissage, à la fois sur le campus et hors campus, et cela pourrait être une retombée positive.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".