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Design, Construction, and Destruction in the Classroom: Experiential Learning with Earthen Dams

2020· article· en· W3012710084 on OpenAlexaff
Bruce MacVicar, Andrew Clow, Chris Muirhead, Rania Al-Hammoud, James Craig

Bibliographic record

VenueJournal of Hydraulic Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsBlackberry (Canada)University of Waterloo
Fundersnot available
KeywordsExperiential learningCurriculumHydraulicsEngineeringEngineering educationSpillwayCivil engineeringExperiential educationTest (biology)Engineering managementMathematics educationGeotechnical engineeringPsychologyPedagogyGeology

Abstract

fetched live from OpenAlex

Early in their career, engineering students sometimes have difficulty linking academic concepts between courses and the interests that led to their enrollment. We present a design “interlude” event that is intended to improve experiential inductive learning and the vertical and horizontal integration of engineering courses. In this exercise, second year engineering students design, build, and test a scaled model of an earthen dam. The method integrates content from technical courses in which they are currently enrolled, such as statistics and fluid mechanics, and previews future courses in their curriculum, such as soil mechanics, hydrology, and hydraulics. Students consult with a range of experts, mock stakeholders, and mock protestors. Projects are evaluated for social/environmental impacts, technical design, cost, and performance, including stability, power generation, and spillway overflow. Student feedback was positive and highlights the intended benefits for the students, including interaction with industry partners, team building for the students, hands-on learning, and a better understanding of the impact of engineering projects.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.448

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 integrity0.0000.001
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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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