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Record W2739090830 · doi:10.1080/02680513.2017.1354762

Adoption of open educational resources (OER) textbook for an introductory information systems course

2017· article· en· W2739090830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOpen Learning The Journal of Open Distance and e-Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsSaint Mary's University
FundersMcMaster University
KeywordsOpen educational resourcesProcess (computing)Open educationComputer scienceWorkloadWork (physics)Mathematics educationKnowledge managementLibrary sciencePsychologyWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Open educational resources (OER) can make educational resources widely available to all students and educators for free; however, OER are still untried in many academic programmes in higher education. This article reports a case of adoption of an open access textbook for an introductory information systems course and discusses the process and suggestions of adoption of an OER textbook based upon the authors' own experience. The study indicates that the process of adoption of an open access textbook demands more intellectual work on the instructors' side in comparison with the adoption of a new commercial textbook. The study suggests that discipline-based communities of practice (CoP) can ease the workload problem in the process of adopting OER textbooks. The findings are important in encouraging the OER community to shift from project-based OER textbook development to discipline-based CoP for effective OER textbook adoption.

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.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0110.016
Open science0.0070.002
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 head0.030
GPT teacher head0.335
Teacher spread0.306 · 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