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Record W2064662029 · doi:10.1198/000313001317098239

A Computer-Based Lab Supplement to Courses in Introductory Statistics

2001· article· en· W2064662029 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.

Bibliographic record

VenueThe American Statistician · 2001
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsAcadia University
Fundersnot available
KeywordsFrequentist inferenceComputer scienceCurriculumMathematics educationSimple (philosophy)MathematicsPsychologyArtificial intelligencePedagogyBayesian probabilityBayesian inference

Abstract

fetched live from OpenAlex

The computer continues to assume a role of increased importance in university education, and professors must determine appropriate means for its integration into the curriculum. This article describes the incorporation of a studio lab component into undergraduate courses in introductory statistics. We detail the objectives of these courses and describe the motivations, general structure, and main features of our approach. The labs typically involve a two-step frequentist approach where a simple hands-on experiment is performed that is subsequently replicated using the computer. We describe in detail two labs that typify the main features of our approach, and discuss the exibility of the labs with regard to the target audience. A discussion of our perception of their impact on student learning is given, along with some comments on alternative modes of delivery.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.301
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.111
GPT teacher head0.430
Teacher spread0.320 · 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