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Record W1534172683 · doi:10.22329/celt.v3i0.3259

29. Two Models for Introducing Graduate Students to the Scholarship of Teaching and Learning

2010· article· en· W1534172683 on OpenAlexaffvenue
Natasha Patrito Hannon, Svitlana Taraban-Gordon

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of WaterlooWestern University
Fundersnot available
KeywordsScholarship of Teaching and LearningScholarshipGraduate studentsMathematics educationTeaching and learning centerPedagogyTeaching methodPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Graduate students aspiring to become faculty members should be provided with meaningful opportunities to explore the scholarship of teaching and learning (SoTL) and to formulate questions about student learning and effective teaching. To this end, teaching and learning centres should incorporate SoTL-oriented components within the framework of educational development programs to prepare our future faculty. This article briefly reviews the emerging literature on graduate student engagement with SoTL and highlights two possible approaches for incorporating SoTL into educational development programs for graduate 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)0.008
metaresearch head score (Gemma)0.016
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: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.015
Scholarly communication0.0080.009
Open science0.0030.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0150.005

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 head0.092
GPT teacher head0.434
Teacher spread0.342 · 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
GenreMethods

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

Citations1
Published2010
Admission routes2
Has abstractyes

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