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Record W4415780937 · doi:10.64710/hfls9668

Examining the effectiveness of the numeracy course ‘Numbers for Life’ at McMaster University

2025· article· W4415780937 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAdults Learning Mathematics An International Journal · 2025
Typearticle
Language
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsGeorge Brown CollegeUniversity of Toronto
Fundersnot available
KeywordsNumeracyVariety (cybernetics)Class (philosophy)NarrativeCourse (navigation)Qualitative propertyGovernment (linguistics)Logical reasoning

Abstract

fetched live from OpenAlex

This paper reports on our study of the effectiveness of the course instruction in the numeracy course ‘Numbers for Life’, offered at McMaster University, Canada, and taught by one of the co-authors. In this course students explore the ways to reason with numbers in a variety of contexts important not only for their individual lives and their community, but broader – it helps them understand the world they live in, and the challenges they will face. To answer important questions about students’ learning and their development of quantitative reasoning skills, we designed a two-year research project, for which we secured government funding. We combined quantitative and qualitative data analysis methods to assess the gains in student learning and skills development using a number of instruments, including pre-test and post-test surveys, class activities, course assessments and teaching evaluations. Our research suggests that the “Numbers for Life” course instruction improves most students’ numeracy knowledge and skills. We detected improvements in students’ ability to understand numbers, ability to engage with logical constructions and reasoning, and ability to engage with multiple-step problems which require quantitative reasoning. The largest learning gains were detected among students who had inadequate background, as determined by their pre-test performance. We were not able to detect pre- to post-test improvements in communication (explaining what a numeric answer represents, providing a logical argument, creating a narrative about a situation involving numbers, and so on). We believe that part of the reason for this lies in the fact that students were probably not as serious, nor as patient in their approach to completing their post-test (at the end of the semester) as they were in completing a pre-test (at the start of the semester).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.324
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it