Taxonomy of purposes, principles, forms, technologies in on-line education: comparative analysis of virtual universities: pedagogical aspects
Bibliographic record
Abstract
Importance. The paper presents the findings of comparative pedagogical research done against the backdrop of new tendencies and developments on the global learning landscape. With the pandemic pushing universities online and ensuing digitalization of educational environment with a flurry of multi aspect virtual learning activities, there has emerged a growing need for reliable criteria to assess effectiveness and efficiency in higher education. The research is aimed to build the taxonomy of virtual universities purposes with the focus on the paramount principles, educational forms and technologies they employ. Research Methods. The research methods involved comparative, descriptive, inductive-deductive ones, typical of comparative pedagogy. We studied the purposes, principles, forms and technologies used by the following virtual universities: The UK’s Open University), Canadian Virtual University (CVU), The University of Phoenix (UoP), The Virtual University for Small States of The Commonwealth (VUSSC), The Virtual University of Pakistan. Results and Discussion. The research enabled us to aggregate, analyze and compartmentalize the data on purposes, principles, forms and technologies of different virtual universities, which offers a new viewpoint on i-learning in a new setting. The purposes of virtual universities are presented in the hierarchical order in compliance with the goal-setting levels: expediency, focus (purposes on this level are differentiated according to the type of activity: educational, pedagogical, maintaining and organizational) and goal commitment. We identified a set of basic principles regulating academic activity of virtual universities in the digitalization context of education, described the most popular forms and technologies employed and specified priority activities of virtual universities. Conclusion. The taxonomy of purposes and paramount principles of virtual universities educational activities make these activities unique. The number of factors and variables defining the educational activities of virtual universities is growing, still the ability to choose the learning route which fits a certain individual has always been and will remain important. Tailored learning style (built on individual preferences in the ways of information searching and processing) suggests an opportunity to adjust the situation and learning material to your needs and thus become more efficient in obtaining new information and skills.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".