What do Students Learn in the Numbers for Life Course at McMaster University? Assessing students’ improvement and retention of numeracy knowledge and skills
Bibliographic record
Abstract
Our society is surrounded by numbers and throughout our lifetime we all experience numeric situations daily. Developing necessary numeracy skills is a crucial part of being able to fully participate in modern technological society and engage in the world around us. The course Numbers for Life at McMaster University (Math 2UU3) is designed to teach about critical numeracy problems that we are faced with in our daily lives and is offered to non-mathematics major students in their second year or above. Students in the course were surveyed three times through a pre-test, post-test and delayed post-test, that was written one year after course completion. Using the responses to these survey instruments, this thesis focuses on studying the retention of a student's ability to understand numeric information and their ability to communicate their answers. Having good retention is key for a learner to successfully apply what they have learned in future real-life scenarios. By studying the retention of students' responses to commonly encountered real-world math problems, we can determine how valuable courses like Numbers for Life are to have in place for all students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".