Bibliographic record
Abstract
Learning styles attempt to describe individual differences among students by identifying students’ preferences in how they learn, and adapting their learning to accommodate that style. Since their inception, learning styles have gained mass popularity among teachers, researchers, and the public. Numerous assessments and self-help books are available to discover one’s individual learning style. Learning styles, however, have been heavily criticized by researchers who contend that learning styles lack evidence supporting their effectiveness and possess unreliable diagnostic tools. I posit that the case against learning styles is not limited to those two claims; in addition, that learning styles outcomes can be associated with confounding factors, and that learning styles may lead to ineffective teaching practices that negatively affect students and teachers. Through evidence-based practices, we can move forward from learning styles and create learning environments that have a greater probability of positive effects.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".