Psychosocial Outcomes from Self-Directed Learning in Public Education Settings
Bibliographic record
Abstract
Self-directed learning in different public education settings can produce positive psychosocial outcomes if learners accept their own and others’ right to self-direct their learning regarding what they value in a community that demonstrates team mindfulness. Successful self-directed learning is possible at diverse academic levels and in various public education settings. The evidence is that the author co-founded three such educational initiatives. The author assesses the total works published since 2020 regarding these initiatives using narrative analysis. One result of this investigation is that for these initiatives to succeed online, a participant-trusted facilitator who takes on the role of an authentic leader is necessary. Lacking such a facilitator, participants may achieve positive psychological outcomes, but they will not realize the positive sociological outcomes from a well-functioning group decision-making method based on consensus decision-making available to self-directed learners. Achieving positive sociological outcomes has been found challenging in public educational settings. However, these outcomes are possible when a group has a common self-directed learning goal. Offered are suggestions to achieve positive psychosocial outcomes with self-directed learning in public education.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".