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Record W4403430182 · doi:10.15173/ijsap.v8i2.5555

Transforming teaching assistant roles into co-creators of instruction

2024· article· en· W4403430182 on OpenAlexvenueno aff
Amrita Kaur, Wei Zou, Ziyu An, Yuhao Ma, Kehan Lu, Qingqing Zhou

Bibliographic record

VenueInternational Journal for Students as Partners · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCo-teachingMathematics educationPedagogyComputer sciencePsychology

Abstract

fetched live from OpenAlex

This case study explores the implementation of a collaborative initiative that transformed the traditional role of teaching assistants (TAs) into student-faculty partners in two psychology courses. The objective of the collaboration was to leverage the insights and contributions of undergraduate students as co-creators of instruction for students’ engagement and meaningful learning experience. The case study highlights the processes, impacts, and challenges of these partnerships, revealing opportunities for student partners to develop pedagogical and assessment literacy, enhance communication and leadership skills, and gain insights into student behaviors and preferences. Pedagogical and curricular gains were observed, including the incorporation of student insights into instructional activities and improved teaching materials. However, challenges related to power dynamics and student perceptions of privilege were also identified. The findings emphasize the importance of careful navigation and the creation of meaningful opportunities for student engagement in higher education.

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)0.014
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0100.007
Open science0.0030.011
Research integrity0.0020.003
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 head0.040
GPT teacher head0.585
Teacher spread0.544 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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