Enhancing the Performance of Andragogic Education
Bibliographic record
Abstract
The article is devoted to the development of adult education, its andragogical principles and the impact on the quality of lifelong pedagogical education. The authors examined the mechanisms for improving the quality of lifelong pedagogical education by updating the content, developing and implementing innovative teaching technologies following new European guidelines and concepts in education, taking into account national educational traditions. In the article, the authors considered the basic concepts of andragogy, investigated the specifics of andragogical processes, the world experience in adult education. The authors noted that adult education today is based on continuous learning, self-education, and self-development. People will have to retrain 2-3 times in life; they will retrain at least once in adulthood. The research methodology is based on personality-activity and systematic approaches. As a result of theoretical and methodological research, the authors proposed a mechanism based on information technology (distance learning), the principles of androgynous learning and the specific features of adult learning (which the authors divided into three groups). This mechanism can increase the effectiveness of andragogical education in developing an educational trajectory. Further research is the search for ways of state financing of adragogical education and the development of indicators of its effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".