Does Self-directed Learning Readiness Predict Undergraduate Students’ Instructional Preferences?
Bibliographic record
Abstract
Self-directed learning is a process by which students take the lead, with or without the help of others, in determining their learning needs and managing their learning strategies and outcomes. Relatedly, self-directed learning readiness (SDLR) looks at the attitudes, abilities, and personality characteristics necessary for self-directed learning. In study one, we shortened, and slightly modified, the SDLR scale (Fisher et al., 2001) to make it more applicable for broader use among undergraduate university students and to examine its factor structure and reliability. In a sample of 194 students, the three-factor structure of this scale (self-management, desire to learn, and self-control) was confirmed with acceptable reliability. In study two, we examined whether the modified SDLR subscales predicted a preference for a teacher-directed or student-directed class format in a sample of 256 undergraduate students. We conducted a series of four multiple linear regressions to examine whether the three dimensions of SDLR were predictive of four classroom preference styles (knowledge construction, teacher direction, cooperative learning, and passive learning). While three of these analyses were statistically significant with small to medium-effect sizes, the results minimally supported our hypotheses. We discuss whether these results indicate a lack of relationship between SDLR and teaching style or whether these results may be characteristic of the sample.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".