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Record W2614467873 · doi:10.18260/1-2--170

Benchmarking International Industrial Engineering Programs

2020· article· en· W2614467873 on OpenAlexaboutno aff
Jane Fraser, Alejandro Teran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingAccreditationStandardizationCurriculumInclusion (mineral)Order (exchange)Engineering educationEngineering managementPolitical scienceEngineeringBusinessComputer scienceEconomic growthMarketingSociologyEconomicsFinanceSocial science

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Benchmarking International IE Programs Introduction Fraser6 compared the 101 ABET accredited industrial engineering programs by location, size, and other descriptors, as well as by the inclusion of different courses in the curricula. Except for two programs in Puerto Rico, all these programs are in the United States. In this paper, we extend that comparison to include industrial engineering programs in other countries in order to find ideas that US programs (and programs in other countries that use the US model) should consider for adoption from IE programs outside the US. We found differences in total number of credit hours and in number of years required for the IE degree, in the amount of general education included in the degree, and in the strength of ties to industry. We noted trends toward standardization of degrees in certain countries and regions and toward international links among programs. We make two recommendations related to partners: IE programs should seek partnerships with mechanical engineering and with business programs, and IE programs should seek partners with universities in other countries. Methods for finding IE programs in other countries We compiled a list of programs to be examined by drawing from the following sources. Washington Accord Programs. The Washington Accord, signed in 1989, is an agreement among engineering accrediting bodies in Australia, Canada, Ireland, Hong Kong, New Zealand, South Africa, United Kingdom, and the United States. The agreement “recognizes the substantial equivalency of programs accredited by those bodies, and recommends that graduates of accredited programs in any of the signatory countries be recognized by the other countries as having met the academic requirements for entry to the practice of engineering.” See www.washingtonaccord.org. •Institution of Engineers Australia, www.ieaust.org.au •Canadian Council of Professional Engineers, www.ccpe.ca •The Hong Kong Institution of Engineers, www.hkie.org.hk •Engineers Ireland, www.iei.ie •Japan Accrediting Board for Engineering Education, www.jabee.org •Institution of Professional Engineers New Zealand •Engineering Council of South Africa, www.ecsa.co.za •Engineering Council United Kingdom (ECUK), www.engc.org.uk The following are not members of the Washington Accord, but were useful websites: •ASIIN (Germany), www.asiin.de •CACEI (Mexico), Consejo de Acreditacion de la Ensenanza de la Ingenieria, www.cacei.org.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0180.033
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.012

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 head0.026
GPT teacher head0.197
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2020
Admission routes1
Has abstractyes

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