Development of Students’ Research Competence in the Study of the Humanities in Higher Educational Institutions
Bibliographic record
Abstract
The aim of the study was to experimentally test the effectiveness of didactic conditions for the development of students’ research competence in the study of the humanities. Several complementary methods were used in the experimental study: a comprehensive test to assess the levels of research competence (S.A. Mishyn); Zamfir’s Motivation of Professional Activity modified by A.N. Rean; adapted Starkey’s Critical Thinking test; Simonov’s Education Level technique, author’s questionnaire with open-ended questions to determine the importance of research competence for students in the course of their professional education. The results of the pedagogical experiment revealed the effectiveness of the introduction of innovative forms of theoretical training (lectures and seminars) for the development of research competence in future specialists in the humanities classes. The research found the main directions of innovative experience of improving lectures, seminars as a form of education for the development of students’ research competence. The research showed that it is possible to develop research competence in the process of teaching humanities depending on the types of training (theoretical, practical). It is important to organize the students’ cognitive activity in the course of theoretical training, while it is necessary to apply the problem method of teaching in the practical classes. We consider the study of the problem of continuity in the development of the research culture of the individual under the conditions of continuing education as a prospect for further research.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".