The Influence of Biographical Factors on Adult Learner Self-Directedness in an Open Distance Learning Environment
Bibliographic record
Abstract
This study investigated the relationship between self-directedness (as measured by the Adult Learner Self-Directedness Scale) and biographical factors such as age, race, and gender of adult learners enrolled at a South African open distance learning (ODL) higher education institution. Correlational and inferential statistical analyses were used. A stratified random sample of 1,102 mainly black and female learners participated in the study. The Adult Learner Self-Directedness Scale (ALSDS) identified four constructs of adult learner self-directedness in an Open Distance Learning Higher Education (ODLHE) milieu, namely the strategic utilisation of officially provided resources, engaged academic activity, success orientation for ODLHE, and academically motivated behaviour. The research indicated that significant differences exist between the gender, race and age groups with regard to self-directedness. With regard to gender, males scored significantly higher than females on success orientation for ODLHE and engaged academic activity. With regard to race, Indian participants scored significantly higher than the other race groups on strategic utilisation of officially provided resources and engaged academic activity. The white participants scored significantly higher than the other race groups on success orientation for ODLHE. In terms of age, the age group >50 scored significantly higher than the other age groups on success orientation for ODLHE and self-efficacy. In terms of success orientation, the means for the age groups seem to increase as the ages of participants increase. The age group 18-25 scored significantly higher than the other age groups on engaged academic activity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".