Utilization Trends of the Israeli Higher Education System by Generation Z from 2015-2020
Bibliographic record
Abstract
This study focuses on members of Generation Z, born from the mid-1990s until the end of the first decade of the current century into a world of technology, social networks, and a culture of immediate messaging. The study seeks to examine the effect of this generation’s pragmatic outlook both in general and in the context of acquiring a higher education, on trends involving registration for undergraduate studies. The Israeli system of higher education was chosen as a case study since the rate of Israelis with a higher education is among the highest in the world. Academic studies are perceived in Israel as a crucial milestone and an essential developmental stage in the life course of many young people. Data on the distribution of students among the different disciplines shall be analyzed by correlational examination of changes in these trends in the various degree levels from 2015-2020. The research findings show that from the mid-2010s a drop is evident in the number of undergraduate students. Moreover, a conspicuous increase is evident in the number of students in the fields of medicine and allied health professions, science and mathematics, engineering and architecture, which are considered applied fields, while a decline is evident in the social sciences, the humanities, law, and business administration. These findings point to the tendency of Generation Z to practical and technological studies more than fields considered less practical. The research conclusions call for implementing several regulatory steps in order to adapt the system of higher education to the characteristics and needs of Generation Z, such as expanding the professional training program in less practical disciplines, shortening the duration of studies in technological vocational departments, increasing the use of online teaching, and others.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".