Ways to Enhance Students’ Learning Activities in the Context of Higher Education
Bibliographic record
Abstract
The article aims to study scientific and methodological features of the ways to enhance students’ learning activities in the context of higher education. The study's theoretical significance is derived from the in-depth analysis of the development of students' learning activity. The study offers the framework of characteristics for the concept of “students' learning (cognitive) activity". The main used methods were: analysis, peer-assessment, self-assessment. The interview method was used to make a cognitive profile of the participants, taking into account their psychological characteristics. The authors developed and scientifically tested educational model based on module curricula and interactive teaching methods to enhance students' learning activity. The authors also reflected the dynamic of the learning activity of the students with disabilities participating in the experiment. The results proved the effectiveness of the developed model of enhancing students' learning activity by using interactive teaching methods. It was concluded that the module curricular and active teaching methods help enhance students with disabilities' learning activity and make them more responsible in respect to the results of their study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".