ASSESSING THE IMPROVEMENT IN LOGICAL REASONING OF STUDENTS ENROLLED IN “NUMBERS FOR LIFE” COURSE AT MCMASTER UNIVERSITY
Bibliographic record
Abstract
To be numerate is to have the ability to understand numbers and be confident with numeric information presented in day-to-day situations. The way numeracy is defined varies between researchers; however, most agree that having skills in numeracy is essential to function in the world. In order to provide students with the opportunity for exposure to basic numeracy skills, McMaster University’s course Math 2UU3 – “Numbers for Life” is offered to non-mathematics major students in second year or above. To measure the effectiveness of this course, and to determine whether students retain the numeracy skills and knowledge acquired in the course, we developed a series of assessments with questions based on content learned throughout the semester. Students were tested three times – once before completing the course, once after completing the course, and once again a year later. This study focuses in on the logical reasoning aspect of numeracy which includes understanding logical structures and being able to work through problems rationally and systematically. The results from the study reveal that students who took the course and participated in completing the given assessments showed improvement with their logical reasoning skills significantly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".