Adult learners’ perceptions of self-directed learning and digital technology usage in continuing professional education: An update for the digital age
Bibliographic record
Abstract
Mandatory continuing professional education is accepted across many professions as a re-credentialing mechanism to maintain professional competency. Self-directed learning is a widely recognized type of learning to meet mandatory continuing professional education requirements. The nature and characteristics of self-directed learning has been transformed with the growth in digital and mobile technologies, however there is minimal understanding of the role of these technologies in the self-directed learning habits of adult learners. This study sought to explore the perspectives of adult learners around the effect of digital and mobile technologies on continuing professional education activities. Semi-structured interviews were conducted with 55 adult learners from four professional groups (9 physicians; 20 nurses; 4 pharmacists; 22 social workers). Key thematic categories included perceptions of self-directed learning, self-directed learning resources, key triggers, and barriers to undertaking self-directed learning. Digital and mobile technologies emerged as important resources supporting the self-directed learning of health and human services professionals. Increasing usage and dependency on these technologies has important implications for organizational and workplace policies that can support effective self-directed learning processes in a digital age. A conceptual model is introduced to characterize the key factors defining the self-directed learning patterns and practices of adult learners in a digital age.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".