Adult Participation in Self-Directed Learning Programs
Bibliographic record
Abstract
This paper attempts to explain the various concepts related to self-directed learning and also the various theories and models regarding adult participation and also non-participation in self-directed learning programs. Because of the extensive amount of previous literature and research findings dealing with self-directed learning, it is necessary to synthesize the relevant literature so that it can be useful as a basis for this and also for further research in this field. Conceptualization of self-directed learning will be reviewed in the wider and broader perspective. Also reviewed will be the development of self-directed learning, the definitions and characteristics of self-directed learning. Different conceptualization and factors contributing to adult participation in self-directed learning will be touched. In order to design an instrument and to develop a conceptual model, which adequately reflects those factors that have been reasonably determined to be relevant, it is felt that there was a need to identify those variables or factors, reported in earlier studies, which have been found to be significantly associated with adult participation in self-directed learning.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".