The Research of Educational Motivation of Working and Nonworking Russian Students
Bibliographic record
Abstract
The modern society has established high standards for the system of higher education responsible for the training of highly qualified, competitive specialists. The improvement of the educational process in a higher educational institution, the formation of students’ vocational competence is possible through the realization of students’ educational activity the important component of which is motivation. Such issues as levels of educational motivation, hierarchy and dynamics of educational motives in the course of mastering a profession peculiar to Russian working and nonworking full-time students seeking a bachelor’s degree in the sphere of psychology and pedagogics have been studied in the article. There have been revealed systemic motives in the structure of educational motivation of working and nonworking students at each year of studies. There have been defined problem areas of students’ educational activity, and there have been designated tendencies for further development of students’ educational motivation. The following techniques have been applied as psycho diagnostic tools: “Definition of students’ motivation for studies”, “Diagnostics of students’ educational motivation”. New opportunities for psychological and pedagogical approaches to solve the issues of students’ educational cognitive activity optimization, extra curricular activity within the frames of higher educational institution and outside have been revealed due to the of research of the structure and specificity of educational motivation of students seeking a bachelor’s degree with their secondary employment in view.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".