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Record W4306751876 · doi:10.15173/ijsap.v6i2.4790

Students as assessment partners: A collaborative, qualitative evaluation of the Guns on Campus CURE

2022· article· en· W4306751876 on OpenAlexvenueno aff
Katherine McLean, Samantha Penascino, Jazzmine McCauley, Rachel Russell, Kerian Martinez-Pitre, D. L. Bish, Nathan E. Kruis

Bibliographic record

VenueInternational Journal for Students as Partners · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipScholarshipMedical educationQualitative researchPsychologyCoronavirus disease 2019 (COVID-19)Process (computing)Reflection (computer programming)PedagogyMedicineSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The Students-as-Partners (SaP) paradigm has been widely recognized for its enrichment of pedagogy and research, particularly in the scholarship of teaching and learning; in a time of acute disruption to higher education, the SaP model may further provide key insights into the adaptation of high impact teaching practices, although the changing conditions of partnership require close attention. This paper reports on the qualitative evaluation of a multi-campus, hybrid course-based undergraduate research experience (CURE) delivered in the first year of the COVID-19 pandemic. Analysis of student reflection data was conducted by a research team of two faculty and four CURE-student participants in a process informed by the Students-as-Partners model. In addition to identifying student-reported challenges, solutions, and educational benefits associated with a hybrid CURE, we reflect on both the unique opportunities, and difficulties, offered by student-faculty partnerships formed and conducted in a virtual meeting space.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.162
GPT teacher head0.676
Teacher spread0.515 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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