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Record W2940197051 · doi:10.19173/irrodl.v20i1.3853

University Teachers and Open Educational Resources: Case Studies from Latin America

2019· article· en· W2940197051 on OpenAlexvenueno aff
Virgínia Rodés, Adriana Gewerc Barujel, Martín Llamas Nistal

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
FundersUniversidad de la República Uruguay
KeywordsOpen educational resourcesOpen educationProfessional developmentCurriculumEducational technologySociologyLatin AmericansPedagogyKnowledge managementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The Open Education movement has made efforts to systematise experiences and to evaluate the adoption of Open Educational Resources (OER). However, OER adoption is not part of the prevailing paradigm in higher education, both at the global level and in Latin America. This paper describes results of a study that analysed the social representations regarding the development, use, and reuse of OER by university teachers in their pedagogical practices. We conducted a study of 12 cases from Latin American universities using data analysis based on Grounded Theory. The results show that the use and reuse of OER lacks of public and institutional policies. The main agents are teachers organised in teams that support OER adoption. The reasons that encourage the creation of OER are mainly intrinsic, such as the pleasure derived from contributing and sharing, as well as external and related to professional development needs from the reflection on one’s own educational practice. Educators consider it essential to evaluate the resources created so that they can be reused in continuous improvement processes. Commercial use and misappropriation of the works are two of the main tensions identified. The community factor of teaching guides most behaviours in OER adoption in educational institutions and is presented as an inherent part of the development and transformation of the curriculum.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.000
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 head0.096
GPT teacher head0.436
Teacher spread0.341 · 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 designNot applicable
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

Citations24
Published2019
Admission routes1
Has abstractyes

Explore more

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