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Record W2068558342 · doi:10.1126/science.333.6047.1221

Response—Education Research: Set a High Bar

2011· article· en· W2068558342 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

VenueScience · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAttritionAttendanceContext (archaeology)Test (biology)Mathematics educationSet (abstract data type)Class (philosophy)PsychologyConfoundingComputer scienceMathematicsStatisticsMedicinePolitical scienceHistory

Abstract

fetched live from OpenAlex

Torgerson and Derting et al. 's concerns may be the result of the difference in publication styles between hard sciences and education research literature. Education publications are largely self-contained, whereas research papers in hard science journals present results in a condensed form, requiring readers to be familiar with the relevant literature to fully appreciate the paper. Torgerson and Derting et al. would like to apply experimental design expectations for research in K-12 classes to research in introductory science classes at large universities. However, these practices often are neither necessary nor useful in this context. It is well documented that the student characteristics in a large introductory university science course are remarkably consistent over time [e.g., ([ 1 ][1], [ 2 ][2])]. Barring a change in admissions standards, an introductory science course will have the same very limited and well-characterized slice of the population each year. The teacher content mastery is also uniformly high. The large class sizes further reduce the variability across sections. The number and influence of confounding variables is therefore low relative to the K-12 setting. This makes randomized control trials unnecessary in this setting, particularly if there are relevant pretreatment measures of student performance (as in Table 1). Torgerson raises the possibility of attrition bias. To clarify, the numbers did not reflect attrition, but rather a continuation of the attendance patterns displayed in the previous weeks. The number of students who took the test was consistent with data on previous attendance; in both sections students who took the test had higher overall attendance and higher average midterm scores than those who did not take the test. Both Letters raise concerns that we did not consider how the “teacher effect” might affect our results. References 1, 2, 5, and 10 in our Report show that the characteristics of teachers, other than the pedagogy they use, have little impact on the amount of learning in introductory physics courses. The pretreatment data on the two sections (Table 1) further supports this assertion. Derting et al. argue that well-designed studies must use “validated assessment tools.” Although there is value in using such instruments where possible, we strongly disagree with such a sweeping assertion. First, this would constrain science education research to an extremely small number of topics for which such instruments exist. Second, there is great value in research that demonstrates to faculty members that, by teaching differently, they can help their students perform better on the tests they already use. Finally, we are troubled by the lack of concern with ethical issues in the calls for this study to be replicated in other classrooms before the results can be accepted. This experiment involved real students in a real course. Given the results, we concluded that any student in the control group of such a replication experiment would suffer very real harm to their education. 1. [↵][3]1. E. F. Redish, 2. P. J. Cooney 1. C. Crouch, 2. J. Watkins, 3. A. P. Fagen, 4. E. Mazur , in Research-Based Reform of University Physics, Vol. 1, E. F. Redish, P. J. Cooney , Eds. (American Association of Physics Teachers, College Park, MD, 2007); [www.compadre.org/Repository/document/ServeFile.cfm?ID=4990&DocID=241][4]. 2. [↵][5]1. L. Ding, 2. N. W. Reay, 3. A. Lee, 4. L. Bao , Phys. Rev. ST Phys. Educ. Res. 4, 010112 (2008). [OpenUrl][6][CrossRef][7] [1]: #ref-1 [2]: #ref-2 [3]: #xref-ref-1-1 View reference 1 in text [4]: http://www.compadre.org/Repository/document/ServeFile.cfm?ID=4990&DocID=241 [5]: #xref-ref-2-1 View reference 2 in text [6]: {openurl}?query=rft.jtitle%253DPhys.%2BRev.%2BST%2BPhys.%2BEduc.%2BRes.%26rft.volume%253D4%26rft.spage%253D010112%26rft_id%253Dinfo%253Adoi%252F10.1103%252FPhysRevSTPER.4.010112%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [7]: /lookup/external-ref?access_num=10.1103/PhysRevSTPER.4.010112&link_type=DOI

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)0.056
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0560.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.004
Scholarly communication0.0000.001
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 head0.487
GPT teacher head0.568
Teacher spread0.082 · 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