Building an inclusive definition of e-learning: An approach to the conceptual framework
Bibliographic record
Abstract
E-learning is part of the new dynamic that characterises educational systems at the start of the 21st century. Like society, the concept of e-learning is subject to constant change. In addition, it is difficult to come up with a single definition of e-learning that would be accepted by the majority of the scientific community. The different understandings of e-learning are conditioned by particular professional approaches and interests. An international project, based on the participation of experts around the world, was undertaken to agree on a definition of e-learning. To this end, two main research activities were carried out. First, an extensive review was conducted of the literature on the concept of e-learning, drawing from peer-reviewed journals, specialised web pages, and books. Second, a Delphi survey was sent out to gather the opinions of recognised experts in the field of education and technology regarding the concept of e-learning with a view to reaching a final consensus. This paper presents the outcomes of the project, which has resulted in an inclusive definition of e-learning subject to a high degree of consensus that will provide a useful conceptual framework to further identify the different models in which e-learning is developed and practiced.
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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 | 0.045 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".