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Record W2782701247 · doi:10.1108/ijshe-07-2017-0111

Systematic curriculum integration of sustainable development using life cycle approaches

2018· article· en· W2782701247 on OpenAlexaffabout
Bastien Roure, Chirjiv Anand, Véronique Bisaillon, Ben Amor

Bibliographic record

VenueInternational Journal of Sustainability in Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCurriculumSustainable developmentCurriculum mappingCurriculum developmentAction planEducation for sustainable developmentEmergent curriculumEngineering managementEngineering ethicsAction researchEngineeringProcess managementMathematics educationPedagogyPsychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to provide a consistent and systematic integration framework of sustainable development (SD) in a civil engineering (CE) curriculum, given the connection between the two. Curriculum integration is a challenging project and requires the development of certain protocols to ensure success. Design/methodology/approach This paper thus proposes a framework for the systematic integration of SD through the lenses of life cycle approach and associated tools to attain effective curriculum integration. The proposed framework suggests the following five steps: mapping the curriculum, setting learning targets, developing an action plan for the assessed program, implementing the action plan and assessing the final performance. Findings This framework was applied to the CE curriculum at Sherbrooke University. To assess its success, a student satisfaction survey was conducted, and teachers’ feedback was obtained; the results showed 85 per cent positive responses. The authors show how this study allowed the CE curriculum to be properly updated and brought in line with today’s engineering profession requirements with regard to SD. Originality/value The integration focuses on the application of life cycle approaches and tools such as environmental life cycle assessment and life cycle costing on CE content. Additionally, the presented approach can be easily adapted to other engineering curriculums and, to a certain extent, to other non-engineering curriculums.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.370
Teacher spread0.319 · 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 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

Citations27
Published2018
Admission routes2
Has abstractyes

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