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Record W2633280124 · doi:10.22329/celt.v10i0.4750

Faculty Teaching Practices and Perceptions: Comparative Analysis Based on Time Spent Lecturing

2017· article· en· W2633280124 on OpenAlexafffundvenueabout
Gülnur Bírol, Adriana Briseño‐Garzón, Andrea Han

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of British Columbia
FundersUniversity of Saskatchewan
KeywordsPerceptionClass sizeInstitutionMedical educationPsychologyHigher educationClass (philosophy)Faculty developmentTeaching methodScale (ratio)Time managementPedagogyMathematics educationProfessional developmentSociologyMedicineManagementPolitical scienceSocial scienceComputer scienceGeography

Abstract

fetched live from OpenAlex

The University of British Columbia-Vancouver (UBC-V) implemented a campus-wide survey of faculty teaching practices and perceptions. All 11 Faculties participated, resulting in a total of 1177 responses for an overall response rate of 24%. We compared response patterns of faculty who reported spending less than 25%, between 26-50%, between 51-75%, and more than 75% of classroom time lecturing. Using this breakdown, we analysed survey responses related to in and out-of-class practices and expectations for students, use of teaching assistant time, participation in professional development opportunities, and perceptions of whether the institution valued teaching. Participants across quadrants reported employing a wide range of teaching methods irrespective of years of experience and class size. Our findings outline the range of teaching practices employed by faculty at a large research-intensive Canadian institution and may provide baseline information for institutions of similar scale and focus.

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)0.006
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0160.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.119
GPT teacher head0.464
Teacher spread0.346 · 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.

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

Citations1
Published2017
Admission routes4
Has abstractyes

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