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Record W4390479093 · doi:10.18357/otessaj.2023.3.2.40

Faculty and Student Perceptions of Open Pedagogy: A Case Study From British Columbia, Canada

2023· article· en· W4390479093 on OpenAlexaffvenueabout
Melissa Ashman

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsContext (archaeology)PerceptionPedagogyQualitative researchMedical educationHigher educationPsychologySociologyPolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

A transformation in teaching and learning happens when students move from being consumers to creators of knowledge. While there is a growing body of research available on the use of open education resources by faculty and students, there is comparatively little research available with regards to open pedagogy (OP) in higher education. The few studies that have explored the perceptions of OP have focused on one specific OP practice in a small context (one or two course sections). The present review study surveyed the perceptions of faculty and students at a Canadian university across several courses and a range of types of OP. Quantitative and qualitative analyses revealed students and faculty alike were positive about the benefits and impacts of engaging in OP, but each expressed challenges with needing greater time for OP. Additionally, while students experienced challenges with process, faculty experienced challenges with supports. Recommendations are provided for ways faculty can support students when engaging in OP and ways institutions can support faculty who engage in OP. Ultimately, knowing more about the experiences and perspectives of students and faculty could help inform the development of best practices for faculty who wish to use OP with 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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.374
Teacher spread0.343 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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