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Record W2600346797 · doi:10.18260/1-2--9220

Enhancing Technology Development Through Lifelong Education Of Engineers And Technologists As Creative Professionals

2020· article· en· W2600346797 on OpenAlexaffabout
Thomas Stanford, Michael Aherne, Duane Dunlap, Mel I. Mendelson, Donald Keating

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLifelong learningEngineering educationProcess (computing)Engineering managementGraduate educationEngineeringEngineering ethicsHigher educationManagementSociologyComputer sciencePedagogyPolitical 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 Session 2793 Enhancing U.S. Technology Development Through Lifelong Education of Engineers and Technologists as Creative Professionals D. A. Keating, 1 T. G. Stanford, 1 D. D. Dunlap, 2 M. J. Aherne, 3 M. I. Mendelson 4 University of South Carolina 1/ Purdue University 2/ University of Alberta 3 Loyola Marymount University 4 Abstract There is growing recognition worldwide that traditional graduate engineering education neither fits the engineering innovation process necessary for competitiveness in the global economy nor reflects the way that graduate engineers and technologists learn and develop as professionals, innovators, entrepreneurs and leaders in industry. In today’s global economy, engineering innovation is recognized as a continuous, systematic needs-driven process, which is highly dependent upon the provision for lifelong learning, growth, and development of the nation’s graduate engineers and technologists in industry beyond their entry-level undergraduate baccalaureate preparation. Because of profound changes in engineering practice for real-world innovation, a transformation is underway in the U.S. Science and Engineering (S&E) innovation system. A concurrent, nonlinear model of needs-driven systematic engineering innovation, which is supported by directed scientific research, is replacing the sequential, linear research-driven model of engineering innovation. Graduate education must be responsive to this change and must build a new type model of in-service graduate professional education which reflects the substantial changes and characteristics of the engineering innovation process itself, and the stages of lifelong growth, professional dimensions, and leadership responsibilities associated with the modern practice of creative engineering in a knowledge-based, innovation-driven economy. Whereas traditional research-based graduate engineering education and teaching have resulted during the last three decades as a byproduct of the linear research-driven model of innovation, a new model of graduate professional education has been developed which focuses on lifelong professional education for emerging and experienced engineering leaders in industry as creative problem-solvers, technical program makers, technology policy makers, and leaders in the modern context of engineering practice for creative technology development and innovation. 1. Introduction More than ever, science, engineering, and technology are key to economic performance and social well being of industrialized nations. The ability to continuously create, develop, and innovate new and improved technology is rapidly becoming the major source of competitive advantage, worldwide, for sustained economic growth. The United States faces stiff competition in the global arena as other nations are also recognizing that growth performance in the new economy is dependent upon technological innovation. There is growing awareness, however, that fundamental changes have occurred in the 1990s with regard to the technological innovation process itself, and a new model of engineering innovation has emerged. Proceedings of the 2001 American Society for Engineering Education Annual Conference & Exposition Copyright ‹ 2001, American Society for Engineering Education

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.248
Teacher spread0.241 · 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 designBench or experimental
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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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