Bibliographic record
Abstract
Most leading universities are using various elements of distance learning. Preparation of high-quality educational materials for the needs of studies, courses and training still consumes the majority of financial resources allocated by institutions for activities in the field of distance learning. The lack of a well-thought-out e-learning strategy for the creation of knowledge resources is a barrier that hinders the sharing of knowledge between training institutions - such as universities, schools and training companies - from various sectors of education, business and administration. This results in a waste of resources, as institutions invest repeatedly in creating the same content. An attempt to solve this problem has led to the development of the concept of learning objects - independent components of e-learning courses that can be used in various distance learning environments and in various educational contexts. The authors of this article have examined whether there is a chance to create an internal university repository of teaching materials that university employees can use. This research is the result of 21 years of work on implementing e-learning at the European University. During research on learning objects, experience was gained from universities in the Netherlands (Open University of the Netherlands, University of Twente), Canada (Athabasca University) and the USA (California State University, Brigham Young University).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".