Are Universities Ready to Recognize Open Online Learning?
Bibliographic record
Abstract
Fast development of technologies, changing needs of digital learners and other aspects of the digital era have had a major impact on universities and their learning management procedures. Access to information online, possibilities of open online learning and need to manage one’s time lead to the changed profile of today’s students and their need to recognize their prior knowledge or skills. This brings a challenge for universities to adapt their procedures of prior learning recognition. This research aims at identifying requirements for universities to recognize open online learning (OOL), focusing on the qualitative analysis of insights and experiences of experts who are knowledgeable and experienced in the field of OOL. Although OOL recognition procedures tend to be similar as in the recognition of other types of learning, the universities face external challenges, coming from labour market, as well as reserved, if not negative, attitudes towards openness and lack of trust in OOL by traditional universities, thus distinguishing the OOL recognition process as being far from accepted practices. The research findings highlight several prospective requirements for universities set to recognize OOL.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".