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Record W4416460723 · doi:10.1080/13504622.2025.2585868

Empowering university teachers for Education for Sustainable Development: the impact of a massive open online course

2025· article· en· W4416460723 on OpenAlexaff
Alejandro Álvarez, Marco Rieckmann, Marisol Lopera Pérez, Louis Volante, Patricia M. Aguirre

Bibliographic record

VenueEnvironmental Education Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsBrock University
FundersDeutscher Akademischer Austauschdienst
KeywordsEnvironmental educationSustainabilitySustainable developmentOnline courseCourse (navigation)Qualitative researchEducation for sustainable developmentHigher educationElectronic learningTeaching method

Abstract

fetched live from OpenAlex

This study seeks to evaluate the efficacy of a massive open online course (MOOC) in promoting Education for Sustainable Development (ESD) competences among university teachers. An exploratory study was conducted using mixed methods under the constructive alignment approach. This involved the creation and delivery of a free MOOC focusing on ESD and offered to educators within Latin American higher education institutions. The constructive alignment approach and the CoDesignS ESD framework, supplemented with theoretical sustainability and ESD knowledge, guided the course. Data from 306 participants were collected and analysed, including pre–post surveys with follow-up assessment and participants’ feedback. The study revealed that university teachers exhibit substantial enthusiasm and motivation to engage with ESD concepts. A MOOC strategy shows promise in strengthening the cultivation of ESD competences among university teachers, especially when combined with hands-on practical components. Despite the need for training educators in ESD and the potential of online platforms to reach diverse audiences, the impact of digital education initiatives for ESD educator training remains largely unexplored. The study is also distinct in its focus on the Latin American context.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.492
Teacher spread0.454 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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