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Record W2836360703 · doi:10.19173/irrodl.v19i3.3390

Adoption of Sharing and Reuse of Open Resources by Educators in Higher Education Institutions in the Netherlands: A Qualitative Research of Practices, Motives, and Conditions

2018· article· en· W2836360703 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsReuseQualitative researchOpen educationKnowledge sharingHigher educationKnowledge managementPublic relationsBusinessSociologyPedagogyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

To find out what is needed to speed up the adoption of open sharing and reuse of learning materials and open online courses in publicly funded Dutch institutions of Higher Education, a qualitative research study was conducted in fall 2016. This study examined issues of willingness of educators and management, barriers and enablers of adoption, and the role of institutional and national policy in the adoption of open sharing and reuse of learning materials and online courses. Fifty-five stakeholders (educators, board members, and support staff) in 10 Dutch Higher Education Institutions were interviewed. The main findings of this study are: motivation for sharing and reuse of learning materials for educators and managers is directly related to the ambition to achieve better education for students; sharing and reuse of learning materials is common practice, very diverse and not open accessible for the whole world, and important barriers include lack of awareness of opportunities for open sharing and reuse and lack of time. Based on the findings from the interviews, the last section of this paper presents conclusions and recommendations regarding how Dutch institutions for Higher Education can formulate effective policies to raise awareness, organize adequate support and provide time to experiment.

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)0.015
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.324
GPT teacher head0.581
Teacher spread0.257 · 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