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Record W4399051375 · doi:10.1080/0020739x.2024.2353895

Postsecondary students’ understanding of statistics through story-based tasks

2024· article· en· W4399051375 on OpenAlex
Collette Lemieux, Olive Chapman

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.

Bibliographic record

VenueInternational Journal of Mathematical Education in Science and Technology · 2024
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsMathematics educationPostsecondary educationStatistics educationPsychologyStatisticsMathematicsHigher educationPolitical science

Abstract

fetched live from OpenAlex

This paper is based on a study that explored a pedagogical approach consisting of interactive story-based tasks used in a first-year undergraduate business statistics course. It addresses the types of understanding students developed for selected statistics topics. Participants were undergraduate business majors required to take the course. Data consisted of the students’ work with the four story-based tasks. Findings suggest that interactive story-based tasks have the potential to be a worthwhile pedagogical approach to support students’ understanding of several statistics concepts at various levels of understanding, but further research is needed to better understand the pedagogical approach needed for adult learners to better implement these tasks to support the deepest level of understanding for all concepts.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.461
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.490
Teacher spread0.349 · 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