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Record W4385829007 · doi:10.1080/13562517.2023.2244887

Faculty beliefs and the need for teaching improvement: a conceptual replication study

2023· article· en· W4385829007 on OpenAlexaffabout
Heather Kanuka, Erika E. Smith, Robert W. Luth

Bibliographic record

VenueTeaching in Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMount Royal UniversityUniversity of Alberta
Fundersnot available
KeywordsPsychologyReplication (statistics)Higher educationTeaching methodQuality (philosophy)Adaptation (eye)Teaching and learning centerClass (philosophy)Mathematics educationMedical educationPedagogySociologyPolitical scienceComputer scienceLawMedicine

Abstract

fetched live from OpenAlex

This study explores faculty beliefs about teaching and learning in different institutional settings and over time. This study surveyed faculty at two Canadian universities, one research-intensive, the other teaching-intensive, using a conceptual replication of a survey originally administered in 1976. Some results differ from the original survey, but most striking are the similarities. While much has changed in higher education over the last 40+ years, including demographic compositions at Canadian universities, the findings reveal the majority of faculty continue to believe: their teaching is very good or outstanding; it is difficult to reward good teaching; the responsibility for teaching quality rests with individual instructors; the most important motivator for teaching improvement is personal satisfaction; the most important ways to improve teaching are through updating course materials and reducing class sizes. These results provide further insights into why changes to teaching beliefs and in turn, teaching practices, are difficult to achieve.

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)0.038
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.006
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.193
GPT teacher head0.493
Teacher spread0.301 · 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
DomainReproducibility
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
Published2023
Admission routes2
Has abstractyes

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