SCIENTIFIC RESEARCH IN THE EDUCATION FIELD AS A CONDITION OF PROVIDING QUALITY OF STUDY IN UNIVERSITIES OF THE WORLD
Bibliographic record
Abstract
The article is devoted to the analysis of scientific researches of the best universities in the world providing training in the field of education and their impact on the quality of education. The analysis is based on the results of the QS World University Rankings in Education, which identified the top ten universities in the world in 2019, which train specialists in education. In the article the general directions and topics of scientific researches, as well as specific ones, specific to each university are highlighted. The author reveals the peculiarities of the research work of the best universities in the world in the field of education and its relationship with the quality of education. Particular attention is paid to the criteria of quality (success) of the activities of universities, and research in particular. The article emphasizes that analyzing the educational research of the best universities in the world as a condition of ensuring the quality of education can be very useful for the development of education (and not just pedagogical) in Ukraine. It is noted that a high level of educational research will help Ukrainian education to achieve modern quality of study, to provide it on a research basis and to become practically oriented. The author defines the key characteristics of successful research universities, including the following: • availability of basic and applied research in contemporary areas and topics; • carrying out research work focused on the practical results of the research; • a wide range of disciplines included in the educational program in any specialty; • a high proportion of postgraduate research programs; • high level of external income of the university, which is ensured by the implementation of research results; • international recognition of research findings and prospects for their further development. According to the QS World University Rankings, the top ten universities in the world that provide training in education are: 1. University College London, (UCL), (United Kingdom); 2. Harvard University, (USA); 3. Stanford University (USA); 4. University of Oxford, (United Kingdom); 5. University of Cambridge, (United Kingdom); 6. University of Hong Kong, (Hong Kong); 7. University of Toronto, (Canada); 8. Berkeley University, California, (USA); 9. Columbia University, New York, (USA); 10.University of California (Los Angeles), USA By looking at research topics, you can distinguish topics that are most commonly found in universities. In this case, we are referring to non-standard general topics of pedagogical research such as: educational policy, organization and improvement of training, development of standards of teaching, didactics of learning, etc. They are present in the scientific research of the best universities, but the most important place is occupied by the research topics that characterize the current stage of development of education in the world, with all its features, influences and main trends. For example, almost all the best universities in the world are researching on human rights and equity in education. A striking example of such research can be the scientific theme of the Pedagogical Institute of Hong Kong University "Justice and Social Justice in Education". Another important theme that unites the best universities is the topic of developing critical thinking and developing critical media literacy skills for students and students. An example of such research is the Teachers' Training Program for Critical Media Literacy Skills in Students at the Teachers at the University of California, Los Angeles, USA.
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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 | 0.041 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".