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Record W2799976694 · doi:10.19173/irrodl.v19i3.3723

Eight Patterns of Open Textbook Adoption in British Columbia

2018· article· en· W2799976694 on OpenAlexaffvenueabout
Jennifer Barker, Ken Jeffery, Rajiv S. Jhangiani, George Veletsianos

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsKwantlen Polytechnic UniversityRoyal Roads UniversityBritish Columbia Institute of TechnologyDouglas College
Fundersnot available
KeywordsPublic relationsOpen educational resourcesPolitical scienceHigher educationOpen educationSet (abstract data type)SociologyBusinessPedagogyComputer science

Abstract

fetched live from OpenAlex

Since the launch of the BC Open Textbook Project in 2012, the adoption of open textbooks has steadily grown within public post-secondary institutions in British Columbia, Canada. An analysis of adoption records over a five-year period reveals that open textbooks have been adopted across all types of institutions, including research universities, teaching universities, colleges, and institutes, and across a diverse set of disciplines, with the largest numbers in the sciences and social sciences. In this report we identify, describe, and illustrate eight distinct patterns of open textbook adoption. These are: stealth adoption, adoption by infection, committee adoption, sanctioned exceptional adoption, course developer adoption, infection by inter-institutional carrier, creation and adoption, and lone adoption. While these patterns are not intended to be exhaustive, we hope that identifying these patterns provides a useful framework for campus leaders to (a) understand how adoptions occur in their own contexts, (b) identify ways to support further adoptions, (c) recognize that there are multiple ways, and no single path, to supporting the adoption of educational innovations at their institutions, and (d) foster the embrace of wider open educational practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.003
Research integrity0.0000.000
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 head0.072
GPT teacher head0.422
Teacher spread0.350 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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