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Record W2980165515 · doi:10.15173/ijsap.v3i2.3771

A faculty member learning with and from an undergraduate teaching assistant: Critical reflection in higher education

2019· article· en· W2980165515 on OpenAlexvenueno aff
Frank Daniello, Caroline Acquaviva

Bibliographic record

VenueInternational Journal for Students as Partners · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipHigher educationReflection (computer programming)Medical educationPedagogyFaculty developmentPower (physics)Teacher educationQuality (philosophy)PsychologyCritical reflectionMathematics educationProfessional developmentPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

This case study describes a student-faculty partnership between an undergraduate teacher education student and a faculty member of teacher education. This faculty-centric partnership aimed to enhance the faculty member’s critical reflection on his pedagogy in an introduction to teacher education course. In this jointly-written article, we offer student and faculty insights about the process we employed, the outcomes of our teaching and learning together, and the complexities of student-faculty working relationships stemming from power dynamics. We also provide recommendations for faculty and students looking to engage in collaborations. These recommendations center on defining partner roles, using video recordings, and addressing power dynamics between students and faculty within higher education. Drawing from our experience, we suggest that student-faculty partnerships are one fruitful avenuefor improving the quality of instruction in higher education. They require minimum financial resources and can enhance faculty pedagogy, which will benefit current and future students.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.014
Scholarly communication0.0100.008
Open science0.0040.012
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.128
GPT teacher head0.599
Teacher spread0.471 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2019
Admission routes1
Has abstractyes

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