Increasing Students’ Motivation to Learn at Tertiary Educational Institutions
Bibliographic record
Abstract
The article is devoted to the study of the main purpose and features of increasing the students’ educational motivation atthe tertiary educational institution (TEI) in times of urgent need for a total overhaul of the educational paradigm and principles of teaching not only at higher educational establishment but also in the Ukrainian education system as a whole. During the investigation the methods of interdisciplinary approach, system analysis, social-cultural method, social-activity and concrete-historical approach have been applied. An important practical conclusion from the results obtained lies in the fact that the effectiveness of the educational process largely depends on the strategies of educational behavior, used by students. Accordingly, educators (and parents) should not only deliver educational material, but also teach students more effective ways and techniques for capturing it. For instance, the educator may recommend students to make a plan (or scheme) of the material studied or lend assistance to students in developing an individual plan for its mastering. Knowledge about the formation peculiarities of educational motivation at the tertiary educational institutions and personality’s value systems in the process of its professionalization can be used in the educational process to optimize the educational activities of students and improve training of specialists. The obtained results make it possible to clarify the subjective criteria for the effectiveness of students’ learning. Knowledge about the content of the value system of the modern student’s personality and its manifestation in the motivation of educational activity is of practical importance both in terms of self-knowledge and human development, and in terms of organizing professional university and postgraduate training. In the course of investigationit was possible to clarify and specify the theoretical ideas about motivation as a structural-level education, torepresentideas about the values that reflect the attitude to activity as a tool of meeting individual development needs, and their impact on the motivation of educational activities. The motives’ conceptof personal educational activity and values of students at tertiary educational institutions as psychological parameters of professional activity, requiring correlation with life objectives, have been highlighted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".