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Record W3160567827 · doi:10.19173/irrodl.v22i2.5161

What Is Open Pedagogy? Identifying Commonalities

2021· article· en· W3160567827 on OpenAlexvenueno aff
Phil Tietjen, Tutaleni I. Asino

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityConstruct (python library)Open educationMeaning (existential)PedagogyEngineering ethicsSociologyOpen educational resourcesField (mathematics)SustainabilityOrder (exchange)Computer sciencePolitical scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

Open pedagogy has been touted by advocates as a promising expansion of open educational resources because it involves shifting from making resources accessible to impacting the practice of teaching. The allure of the term coupled with its promise to bring greater innovation to pedagogy has led to its widespread use at conferences and publications. However, as the concept has gained increasing levels of popularity, it has also sparked considerable debate as to what it means. For example, how is open pedagogy distinct from other forms of pedagogy such as critical or cultural? What does it mean to practice open pedagogy? Without a clear understanding of its meaning, establishing a solid research foundation on which to make claims about the impact of open pedagogy approaches is difficult. Accordingly, this article argues that the current debate signals the need for the development of robust analytical frameworks in order to construct a cohesive body of research that can be used to advance it as a field of study. To do this, the authors review the literature and identify common characteristics within it. The authors then propose a five-part framework that encourages the long-term sustainability of open pedagogy.

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)0.040
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0250.022
Science and technology studies0.0080.055
Scholarly communication0.0360.051
Open science0.0040.020
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.195
GPT teacher head0.516
Teacher spread0.320 · 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 designQualitative
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

Citations43
Published2021
Admission routes1
Has abstractyes

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Same venueThe International Review of Research in Open and Distributed LearningSame topicOpen Education and E-LearningFrench-language works237,207