The Development of Online Learning in Israeli Higher Education
Bibliographic record
Abstract
The COVID-19 pandemic that swept through the world in 2020 and forced the various higher education institutions in Israel and around the world to promptly embrace the online teaching method, placed on the agenda the question of this method’s efficacy as well as deliberations regarding its future implications. The current study reviews the development of online teaching in Israel’s higher education and examines whether this development derives from an organized and well-formulated public policy with a view to the future or is the result of the constraints and various actors within the free market. In addition, the study presents a case study of an academic institution, examining the opinions of students with regard to the benefits and shortcomings of online teaching. The research findings indicate that the development of online teaching in Israel is the result of needs, constraints, and opportunities that emerged in the free market rather than a result of organized public policy by the Ministry of Education and the Council for Higher Education. Consequently, the study presents the various implications of these unregulated developments for the quality of teaching and for student satisfaction. The study illuminates a thorough discussion that should be conducted by movers of higher education and academic institutions concerning a new effective designation of the campuses following the COVID-19 crisis as well as the distinction between virtual and real-life dimensions of academic teaching.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".