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Record W2947062084 · doi:10.15173/ijsap.v3i1.3669

Becoming partners: Faculty come to appreciate undergraduates as teaching partners in a service-learning teaching assistant program

2019· article· en· W2947062084 on OpenAlexvenueno aff
Gail S. Begley, Becca Berkey, Lisa Schassberger. Roe, Hilary E. Y. Schuldt

Bibliographic record

VenueInternational Journal for Students as Partners · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipMedical educationTeaching and learning centerService (business)Teaching assistantFaculty developmentPsychologyService-learningStudent teachingTeaching methodPedagogyMedicineTeacher educationProfessional developmentStudent teacherPolitical science

Abstract

fetched live from OpenAlex

This study examined the relationships between faculty and their teaching assistants in an undergraduate teaching assistant program developed at Northeastern University in the US to ease the challenges faculty faced in incorporating Service-Learning into their teaching. Feedback from faculty suggested that the undergraduates trained to assist them with purely logistical tasks were becoming partners in teaching. To explore the relationship between faculty and their teaching assistants and better understand how the faculty may have come to view the teaching assistants as partners, we conducted in-depth interviews with faculty across a range of academic disciplines and experience levels who had worked with one or more undergraduate teaching assistants. The data revealed that while the faculty participants did appreciate receiving logistical assistance with Service-Learning, they also benefited from partnering with students as colleagues who supported their teaching more broadly. We also found that faculty viewed the partnership in different ways depending on their level of experience with Service-Learning pedagogy.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.111
GPT teacher head0.589
Teacher spread0.478 · 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 designNot applicable
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

Citations9
Published2019
Admission routes1
Has abstractyes

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