Development and Validation of a Tool for Evaluating Self-regulated and Self-directed Aptitudes of Learning (SELF-ReDiAL)
Bibliographic record
Abstract
Abstract Introduction Self-regulated learning (SRL) and self-directed learning (SDL) are widely studied in education, but debates about their relationship have hindered effective measurement in practice. The recently introduced SELF-ReDiAL framework (self-regulated and self-directed aptitudes of learning) addresses this by framing these as adaptable learning aptitudes, integrating SRL features and insights into SDL. Using this framework, we developed and validated a new tool to assess SELF-ReDiAL—particularly valuable for health students and professionals requiring lifelong learning—bridging educational theory and practice. Methods Guided by the SELF-ReDiAL framework, a 30-item questionnaire was developed and administered to students in health-related disciplines across Australia, New Zealand, the UK, and Canada. Exploratory and confirmatory factor analyses (EFA and CFA) assessed the scale’s content and construct validity. Results Overall, 315 responses were analysed (mean age: 23.20 ± 6.73 years, range: 17–58), including 241 women, 70 men, and 4 individuals using other gender terms. Following EFA, 20 items were retained, yielding a four-factor model: ‘Inquisitiveness’ (31.17% variance explained), ‘Accomplishment’ (4.46% variance explained), ‘Implementation’ (4.11% variance explained), and ‘Independence’ (2.54% variance explained). CFA confirmed model fit ( χ 2 = 374.334, df = 164, p < 0.01, χ 2 / df = 2.283; CFI: 0.91, TLI: 0.896, RMSEA: 0.064, SRMR: 0.0523). Both Cronbach’s alpha and composite reliability closely met the threshold for all factors. Discussion The SELF-ReDiAL model offers a comprehensive perspective on learners’ ability to take ownership of their learning when addressing gaps in professional knowledge. In health education, assessing SELF-ReDiAL helps identify influencing factors and informs strategies to enhance these aptitudes, prompting lifelong learning and ensuring high-quality patient care.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".