Bibliographic record
Abstract
What is a learning style? No one seems to know for sure. The language used by learning style theorists is filled with ambiguities. Price (2004) maintains that “learning style is often used as a metaphor for considering the range of individual differences in learning” (p. 681). Is learning style merely a fanciful metaphor or is it the wave of the future? The research offers mixed results. “Effects on improved test scores with testing conditions matched to student style have been published, but,” Curry (1990) adds, “there are also studies showing no discernible effect attributable to learning style variation” (p. 54). How many distinct learning style models are there? The Coffield (2004) team identified 71 different learning style models, which they subdivided into 13 major and 58 minor models. One of the most popular learning style models comes from Rita and Kenneth Dunn. They have developed an eclectic model featuring 21 (23) different variables that influence a person’s learning style. These variables run all the way from light and temperature to whether the person is analytic or global in his or her thinking. Rita Dunn says about the movement: “I want to convert the world” (Kortland, 2007, p. 8). And well she may. The Dunns’ model is used in the United States, Canada, Great Britain, Australia, and a number of other countries. Is learning style a panacea or a placebo? The jury is still out on that question.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".