An Examination of Canadian Post-Secondary Faculty Beliefs Concerning Learning Strategies
Bibliographic record
Abstract
Students make many decisions in academia concerning learning. One of the most critical among them is what learning strategy to use. This research surveyed faculty members from various Canadian colleges and universities to examine their opinions on the effectiveness of different learning strategies. Although the results were mixed, the overall finding showed a discrepancy between faculty opinions and best evidence. Several demographic factors were examined (for example, the faculty’s highest degree, employment status, number of years teaching, subject taught, and institution type), but none was a meaningful predictor of faculty opinions regarding learning strategy effectiveness. Even though faculty opinions were not in line with recognized evidence, their views provide insight into the learning strategies perceived as beneficial and those that students may be using. Learning is a complicated process that can be affected by numerous factors. Hence, this paper reports on the opinions of Canadian faculty concerning learning strategies that facilitate this process and presents plausible theories to explain the disconnect between their views and the best evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".