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Record W4280600980 · doi:10.1128/jmbe.00208-21

Student Attitudes Contribute to the Effectiveness of a Genomics CURE

2022· article· en· W4280600980 on OpenAlexaff
David Lopatto, Anne Rosenwald, Rebecca C. Burgess, Catherine Silver Key, Melanie Van Stry, Matthew Wawersik, Justin R. DiAngelo, Amy T. Hark, Matthew P. Skerritt, Anna K. Allen, Consuelo J. Alvarez, Sara Anderson, Cindy Arrigo, Andrew M. Arsham, Daron Barnard, James E. J. Bedard, Indrani Bose, John M. Braverman, Martin G. Burg, Paula Croonquist, Chunguang Du, Sondra Dubowsky, Heather Eisler, Matthew A. Escobar, Michael S. Foulk, Thomas C. Giarla, Rivka L. Glaser, Anya Goodman, Yuying Gosser, Adam Haberman, Charles R. Hauser, Shan Hays, Carina E. Howell, Jennifer C. Jemc, Christopher J. Jones, Lisa Kadlec, Jacob D. Kagey, Kimberly L. Keller, Jennifer A. Kennell, Adam J. Kleinschmit, Melissa Kleinschmit, Nighat P. Kokan, Olga R. Kopp, Meg M. Laakso, Judith L. Leatherman, Lindsey J Long, Mollie K. Manier, Juan Carlos Martínez‐Cruzado, Luis F. Matos, Amie J. McClellan, Gerard P. McNeil, Evan Merkhofer, Vida Mingo, Hemlata Mistry, Elizabeth Mitchell, Nathan T. Mortimer, Jennifer Leigh Myka, Alexis Nagengast, Paul Overvoorde, Don Paetkau, Leocadia V. Paliulis, Susan Parrish, Stephanie Toering Peters, Mary L. Preuss, James V. Price, Nicholas Pullen, Catherine Reinke, Dennis Revie, Srebrenka Robic, Jennifer Roecklein‐Canfield, Michael R. Rubin, Takrima Sadikot, Jamie Siders Sanford, Maria Santisteban, Kenneth Saville, Stephanie Schroeder, C. Shaffer, Karim A. Sharif, Diane E. Sklensky, Chiyedza Small, Sheryl T. Smith, Rebecca Spokony, Aparna Sreenivasan, Joyce Stamm, Rachel Sterne‐Marr, Katherine C. Teeter, Justin Thackeray, Jeffrey S. Thompson, Norma Velazquez-Ulloa, Cindy Wolfe, James J Youngblom, Brian C. Yowler, Leming Zhou, Janie Brennan, Jeremy Buhler, Wilson Leung, Sarah C. R. Elgin, Laura K Reed

Bibliographic record

VenueJournal of Microbiology and Biology Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of the Fraser Valley
FundersNational Institute of General Medical Sciences
KeywordsGeneral partnershipPsychologyMedical educationMathematics educationMedicine

Abstract

fetched live from OpenAlex

The Genomics Education Partnership (GEP) engages students in a course-based undergraduate research experience (CURE). To better understand the student attributes that support success in this CURE, we asked students about their attitudes using previously published scales that measure epistemic beliefs about work and science, interest in science, and grit. We found, in general, that the attitudes students bring with them into the classroom contribute to two outcome measures, namely, learning as assessed by a pre- and postquiz and perceived self-reported benefits. While the GEP CURE produces positive outcomes overall, the students with more positive attitudes toward science, particularly with respect to epistemic beliefs, showed greater gains. The findings indicate the importance of a student's epistemic beliefs to achieving positive learning outcomes.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.321
Teacher spread0.303 · 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 designObservational
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

Citations14
Published2022
Admission routes1
Has abstractyes

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