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Record W2211190401 · doi:10.48550/arxiv.1210.3385

Improving student's problem-solving ability as well as conceptual understanding without sacrificing the physics content of a class

2012· preprint· en· W2211190401 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
KeywordsClass (philosophy)Mathematics educationGroup (periodic table)Quarter (Canadian coin)Section (typography)Measure (data warehouse)Motion (physics)Control (management)PsychologyComputer sciencePhysicsArtificial intelligenceQuantum mechanics

Abstract

fetched live from OpenAlex

Four sections of introductory physics for physical scientists and engineers (about 180 students each) are compared. One section, treatment group, was organized so that students worked to learn the classical ideas connecting forces and motion over the first 6 weeks of the 10 week quarter and then used the final 4 weeks to apply those principles to algebraically complicated problems. The other sections learned ideas at essentially the same time as calculations over the entire 10 weeks of the quarter. The treatment group and one of the control sections were taught by the same instructor, had identical curricular materials and this instructor was blind to the comparison measure, the final exam. After controlling for GPA as well as for incoming conceptual understanding, the treatment group was found (with greater than 99% confidence) to perform better on the final exam than the control group taught by the same instructor and, by a similar measure, the treatment group performed significantly better than any other section. The treatment group also had higher conceptual learning gains and so should be better prepared for later learning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
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 head0.316
GPT teacher head0.308
Teacher spread0.008 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2012
Admission routes1
Has abstractyes

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