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Record W3015192999 · doi:10.19173/irrodl.v21i2.4598

Open Textbooks: Quality and Relevance for Postsecondary Study in The Bahamas

2020· article· en· W3015192999 on OpenAlexvenueno aff
Edward C. Bethel

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesRelevance (law)Quality (philosophy)Openness to experienceOpen educationComputer scienceOpen learningDistance educationOpen universityHigher educationElectronic publishingPsychologyMedical educationMathematics educationKnowledge managementTeaching methodWorld Wide WebThe InternetMedicinePolitical scienceCooperative learning

Abstract

fetched live from OpenAlex

Open educational resources (OER), are openly licensed text, media, and other digital and analog assets that are useful for teaching, learning, and research. Recent research has shown that in courses where open textbooks are assigned, students perform as well as or better than students in similar courses with commercially licensed textbooks. Despite cost savings and demonstrated effectiveness, perceptions about quality, relevance, ease of access, and other concerns persist. The objectives of this study were twofold: (a) to develop a practical and reusable measure for evaluating open textbook quality in terms of pedagogy, openness, accessibility, and relevance; and (b) to use the measure to rate the quality and relevance of open textbooks for use in higher education in The Bahamas. The study confirmed the viability of the quality measure as a practical tool to assess open resources and found that the open textbooks studied were accessible and well matched to course content, but of varying quality. More study is needed to explore ways to increase faculty adoption, use, adaption and production of OER.

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)0.013
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0040.002
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 head0.250
GPT teacher head0.519
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2020
Admission routes1
Has abstractyes

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