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Record W4401669815 · doi:10.5539/hes.v14n3p170

A Multiple Case Study of Teaching-Focused Professional Development Programs Offered at Three Different Types of US Institutions of Higher Education

2024· article· en· W4401669815 on OpenAlexvenueno aff
Holly Fortener, Leilani Arthurs, Patrick Shabram, Pierre Lu, Chu‐Lin Cheng, Stephanie Plaza-Torres, Carly Flaagan

Bibliographic record

VenueHigher Education Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsProfessional developmentHigher educationMathematics educationFaculty developmentPsychologyTeaching methodMedical educationPedagogyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Teaching-focused professional development (PD) programs offered at institutions of higher education (IHEs) are uniquely positioned to be levers of change that improve the quality of undergraduate science, technology, engineering, and mathematics (STEM) education in ways that broaden participation in STEM education, workforce development, and career pathways in the United States (US). PD programs and their potential to transform undergraduate STEM education, however, are understudied. This multiple-case study compares suites of PD programs offered at three IHEs in the US: a community college, an emerging research institution, and a research-intensive university. Each suite of PD programs is characterized in terms of program structure, implementation, and potential to transform undergraduate STEM education. The presented results illustrate the existence of a wide range of ways in which PD programs are structured and implemented. A key finding is a suite of PD programs offered at these IHEs has greater potential to transform undergraduate STEM education when embedded in an institutional culture that highly prioritizes the teaching enterprise. Lastly, the results are synthesized into an innovative framework. The framework can be used as a tool to design, implement, and evaluate PD programs so they have greater potential to transform undergraduate STEM education in the US.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.305
GPT teacher head0.513
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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