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Record W2283892846 · doi:10.48550/arxiv.1210.3386

A class where qualitative discussions, coming weeks before computationally complicated practice, helps students' problem solving abilities

2012· preprint· en· W2283892846 on OpenAlexaboutno aff
David J. Webb

Bibliographic record

VenuearXiv (Cornell University) · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsHierarchyQuarter (Canadian coin)Class (philosophy)Mathematics educationSet (abstract data type)PsychologyGroup (periodic table)Field (mathematics)MathematicsComputer sciencePhysicsArtificial intelligencePure mathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Psychologists have long known that an expert in a field not only knows significantly more individual facts/skills than a novice but also has these facts/skills organized into a mental hierarchy that links the individual facts (at the bottom of the hierarchy) together with larger more-encompassing ideas (at the top of the hierarchy). In the Spring quarter of 2012, UC Davis offered 4 sections (about 180 students each) of the first quarter of introductory physics, Physics 9A, covering Newtonian mechanics. One of these sections is a "treatment" group and had the entire 10-week quarter's set of ideas introduced, largely qualitatively, in the first 6 weeks followed by the 4 weeks where students learn to use those ideas to solve the algebraically complicated problems that physicists prize. The other three sections were organized as usual. The treatment group and one of the other sections were taught by the author and were identical (same homework, discussion, lecture, and lab) except for the organization of the content. After controlling for GPA as well as Force Concept Inventory pretest scores, the treatment group was found, with better than 99% confidence, to score higher on the final exam. Some curricular details of the treatment class are discussed in this paper.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.166
GPT teacher head0.362
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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