Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.064 | 0.036 |
| Insufficient payload (model declined to judge) | 0.055 | 0.026 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".