Logic and the Development of Scientific Competencies in First-Year General Education
Bibliographic record
Abstract
Since 2008, Mount Royal University (MRU) has been committed to providing students with capacities for quantitative reasoning and the kind of literacy we have come to associate with the interpretation and assessment of scientific ideas shaping public discourse. After ten years, it was decided to revise the curriculum primarily responsible for supporting this mandate. A notable revision was the addition of a unit on logic in the course “GNED1101: Scientific and Mathematical Literacy for the Modern World”, MRU’s foundational course on quantitative reasoning. Logic was added to improve students’ critical reasoning and ability to assess arguments, especially those made by practicing scientists. Interviews with students who have completed class activities and discussions about the modified curriculum show a positive impact of studying logic on their learning skills such as problem solving, writing and understanding scientific texts along with everyday life events. In this paper, we present the rationale behind this curriculum change, the importance of connecting the study of logic to the study of science in the first year of every degree program, and the share some activities that we use in our classrooms to emphasize the relationship between logic and science. We also present student views gathered through semi-structured interviews.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".