Bibliographic record
Abstract
In this study, 106 women (M age = 23 years) completed a series of questionnaires online assessing personality traits and facets (subscales), learning preferences (Activist, Reflector, Theorist, Pragmatist), and attitudes toward learning preferences.The vast majority of participants in this study believed that students are more likely to have academic success when teaching and learning strategies match their learning style. However, the results of several hierarchical regression analyses found that a large proportion of variance in learning style was accounted for by personality traits or facets. For example, 43% of the variance in the Activist Learning Style was accounted for by higher scores on Extraversion, and lower scores on Conscientiousness and Negative Emotionality. When personality facet scores were used as predictors, the proportion of variance jumped to 55%. Similarly, between 27-31% of the variance in Reflector, Theorist and Pragmatist Learning Style was accounted for by personality facet scores alone. The results are discussed in terms of learning style attitudes and myths pervasive in the literature, and the need for evidence-based practices.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".