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Record W618624379 · doi:10.18260/1-2--7787

Introduction To Engineering Problem Solving A New Course For 1100 First Year Engineering Students

2024· article· en· W618624379 on OpenAlexfundno aff
Walthea V. Yarbrough, Sarah Rajala, Richard L. Porter, Hugh Fuller, Laura Bottomley, Mary Clare Robbins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsnot available
FundersVirginia Polytechnic Institute and State UniversityMcMaster UniversityNorth Carolina Agricultural and Technical State UniversityNorth Carolina State UniversityClemson UniversityNational Science Foundation
KeywordsSession (web analytics)Engineering educationMathematics educationComputer scienceClass (philosophy)Course (navigation)EngineeringMathematicsArtificial intelligenceEngineering managementWorld 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 2353 Introduction to Engineering Problem Solving - A New Course for 1100 First Year Engineering Students Richard L. Porter, Laura J. Bottomley, Mary Clare Robbins, Walthea V. Yarbrough, Sarah A. Rajala, and Hugh Fuller North Carolina State University I. Introduction During the past several years, NC State University has offered several experimental courses designed for the first year student in engineering; IMPEC, an integrated approach to mathematics, physics, engineering, and chemistry 1,2; E123, a mechanical dissection course which is now linked with the first year writing and composition course 3,4; and ECE 292D, a hands-on team based design course offered to upper class students as well 5,6. All were offered as an alternative to the introductory course (E100) that had little academic content, no engineering problem solving, and consisted of a large lecture room format with information dissemination as the major goal. Although the alternate courses were excellent and well received by the students, none could be easily scaled up to accommodate 1100+ freshmen engineering students. In 1996 and again in 1997, a new version of freshmen engineering (E497F) was offered that incorporated many of the elements of the alternative courses 7. This was offered to 250-350 engineering freshmen randomly selected each year. Efforts are described to develop and deliver a freshmen engineering course for all 1100+ freshmen that incorporates most of the intrinsic features of the alternative courses; is firmly based on the ABET 2000 outcomes; stresses integration with other first year courses; and contains elements of written and oral communication, team building, critical thinking, multidisciplinary thinking, and problem solving. The structure of the course includes a weekly lecture in large groups, and a smaller team-based problem-solving laboratory which alternates with a required first year course focused on introduction to the computing environments on campus. The content of the course, selection of topics, texts, and assignments is described. The other component of the course is the problem-solving laboratory facilitated by undergraduate student assistants. The laboratory component is strongly linked with the lecture and has a device- dissection component as well as a project design competition. Incorporation of a meaningful hands-on experience for all students presented a considerable challenge. Facilitation of the laboratory is discussed and the device-dissection component is described in another paper 8. Students' attitudes about engineering and the entire first year experience were assessed using the Pittsburgh Freshman Engineering Attitudes Survey © (PFEAS) 9,10,11.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.202
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.2020.075

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 head0.010
GPT teacher head0.269
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations4
Published2024
Admission routes1
Has abstractyes

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