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Record W4402285663 · doi:10.1080/1360144x.2024.2379531

Assessing the teaching culture on campus: the development and validation of the Institutional Teaching Culture Perception Survey-Faculty

2024· article· en· W4402285663 on OpenAlexafffundabout
Ken N. Meadows, Debra Dawson, Lindsay Shaw, Erika Kustra

Bibliographic record

VenueThe International Journal for Academic Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsBrock UniversityUniversity of WindsorWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerceptionPsychologySurvey instrumentMedical educationPedagogyMathematics educationSociologyMedicineApplied psychology

Abstract

fetched live from OpenAlex

In our study, we investigated the psychometric properties of the Institutional Teaching Culture Perception Survey-Faculty (ITCPS-F), an instrument designed to measure faculty members’ beliefs about their post-secondary institutions’ teaching culture. Institutional teaching culture is the behaviours, beliefs, and common values related to teaching, and it influences both student learning and faculty motivation. We administered the survey to faculty at three Canadian universities and found that: (1) the survey has good to excellent internal consistency, (2) Exploratory Structural Equation Modelling confirmed the six-lever structure of the instrument, and (3) the lever scores were significantly and positively associated with student learning, as measured by a subscale of the Students’ Evaluations of Educational Quality and a student-focussed approach to teaching, as assessed by a subscale of the Approaches to Teaching Inventory . These findings support the reliability and validity of the ITCPS-F.

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)0.030
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
GPT teacher head0.505
Teacher spread0.318 · 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.

Study designObservational
DomainEvaluation
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

Citations3
Published2024
Admission routes3
Has abstractyes

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