Question everything: a critical examination of faculty beliefs concerning learning strategy and learning styles
Bibliographic record
Abstract
Students make many questions and decisions in academia concerning learning. One of \nthe most critical among them is what learning strategy to use. In this study, faculty members \nfrom various Ontario (Canada) colleges and universities were surveyed to examine their opinions \non learning strategy effectiveness and on whether learning styles exist as an advantage for \nlearners. This study compares the opinions of faculty members on learning strategy to the \nevaluation of learning techniques outlined by John Dunlosky’s research team (Dunlosky et al., \n2013) and to the best evidence concerning learning styles as an advantage for learning (Pashler et \nal., 2008; Massa & Mayer, 2006). While several key factors were examined (for example, the \nfaculty’s highest degree, employment status, number of years teaching, and institution type), the \nresults produced mixed evidence for faculty opinions against the best evidence. As well, \ndemographic differences among the groups of teachers were not meaningful predictors of their \nopinions. Even though faculty opinions were not in line with recognized evidence, learning is a \ncomplicated situation, and theories will be presented to examine the disconnect between the \ninstructors’ opinions and the best evidence.
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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 | 0.070 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".