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Record W2615739150 · doi:10.18260/1-2--6713

North American Engineering Education & Academic Exchange: Canada, Mexico, The United States

2020· article· en· W2615739150 on OpenAlexaboutno aff
Thomas R. Phillips

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSession (web analytics)Engineering educationLibrary sciencePolitical scienceSociologyComputer scienceLawWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 3260 North American Engineering Education & Academic Exchange: -- Canada, Mexico, the United States -- Thomas R Phillips, ABET/FlPSE Project Consultant Managing Director, Collegeways Associates (USA) From 1993 to 1996 the author served as ‘External Evaluator’ for the Regional Academic Mobility Program (RAMP), a multilateral exchange program run by the Institute of International Education (IIE). RAMP has brought together 26 institutions in Canada, Mexico, and the United States, moving over 200 students in its first three years. However, only about 12% of the exchanges have involved U.S. students. One of the impediments to recruitment has been a lack of timely, consistent, and useful information on programs. The author obtained a FIPSE/USDE grant to develop a guide, consisting of institutional and program profiles, curriculum tables, and selected course descriptions. French and Spanish materials were translated and converted to a standard format. The resulting Guide contains examples of over 100 Canadian and Mexican engineering programs across seven disciplines. The observations in this paper are based primarily upon information from the RAMP institutions. Engineering Education in North America I wanted to determine how a U.S. engineering student could benefit from studies in Canada or Mexico. Was there a professional rationale to support a marketing concept and strategy for the RAMP program? I soon found similarities among the course descriptions and curriculum charts. The topics listed in the standard engineering courses were much like ours - not surprising with the use of standard textbooks and software. Not so apparent is an emphasis on applied engineering skills that increases as you go from Canada to Mexico. In fact, Mexican universities feel that one of their strengths is a comparatively high percentage of faculty members who teach and work in industry. This is viewed as a positive feature in the preparation of graduates for jobs in Mexico’s “productive sector.” While this approach favors industry, it slows faculty development in Mexican universities. Even some of the larger engineering schools have a comparatively small core of full-time faculty with advanced degrees, and relatively small graduate engineering programs. Mexican mechanical and electrical engineering courses often include topics on design for manufacturing, manufacturing process design and control, fabrication, and applications of computers and electronics to manufacturing. Mexican civil engineering programs emphasize competency in construction, while chemical engineering programs serve the processing industries in chemicals, food, and materials. Mexican programs usually have an “industrial engineering” component, focusing more on the practical problems of industrial plants, facilities, and production management, and less on quantitative methods. Courses in labor law and personnel management are standard requirements. Mexican engineering students are taught design, but- also learn to “install, operate, and maintain” electronic, mechanical, and industrial equipment. Given Mexico’s growing manufacturing base, emphasis on infrastructure development, and the number of U.S. employers with operations in Mexico, a U.S. exchange student could create a valuable, marketable learning experience. In both Canada and Mexico, I saw opportunities for student projects and practical experience that would enhance a resume. 1

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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