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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".