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Record W2131482361 · doi:10.24908/pceea.v0i0.3799

TEACHING CIVIL ENGINEERING DESIGN USING PROJECT-ORIENTED INDUSTRY DRIVEN CAPSTONE COURSES

2011· article· en· W2131482361 on OpenAlexvenueno aff
Waddah Akili

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCapstoneCurriculumEngineering managementEngineering ethicsEngineeringCapstone courseEngineering design processProcess (computing)Engineering educationPedagogyComputer sciencePsychologyMechanical engineering

Abstract

fetched live from OpenAlex

Teaching civil engineering design through senior projects or capstone design courses, with industry involvement and support, has increased in recent years. The general trend toward increasing the design component in engineering curricula is part of an effort to better prepare graduates for engineering practice. While some design projects are still of the “made up” type carried out by individual students, the vast majority of projects today deal with “real-world problems” and are usually conducted by student teams. The paper begins first by briefly reviewing the design as a “thought” process, focusing on several dimensions of “design thinking” and how “design thinking” skills are acquired. Second, the paper reports on the development, implementation, and subsequent evaluation of a senior design course at an international university, where practitioners have played a major role in planning and teaching the capstone course. The new, restructured design course, co-taught by practitioners from the Region, has met its declared objectives and exposed students to professional practice. This industry-driven experience has also provided information with regard to curricular content and capabilities of departmental graduates. In a way, the capstone experience reported on in this paper, serves as a microcosm of the four year program. Experiences and outputs from the course can be used to provide guidance and insights into curricular changes, teaching methods, and exposure to civil engineering practice in the Region; and helps in establishing enduring connections with the industrial sector.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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.

Study designSimulation or modeling
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

Citations1
Published2011
Admission routes1
Has abstractyes

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