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Record W2886297820 · doi:10.24908/pceea.v0i0.10227

DEVELOPMENT OF PEER TEACHING SUPPORTED DESIGN FOR X MODULES FOR SENIOR ENGINEERING DESIGN PROJECTS

2018· article· en· W2886297820 on OpenAlexaffvenue
Grant McSorley

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsFormative assessmentAccreditationEngineeringEngineering managementClass (philosophy)Process (computing)Computer scienceMathematics educationMedical educationPsychology

Abstract

fetched live from OpenAlex

Abstract – This paper presents initial results from a series of modules introducing Design for X (DfX) concepts to student project teams. DfX methods are recognised as a means to facilitate decision making throughout the design process and directly support the “Design” graduate attribute as defined by the Canandian Enginering Accreditation Board.
 In this senior design course, third and fourth year students are integrated into the same project teams, with the aim of promoting peer learning and leadership. Therefore, a flipped classroom approach was applied to the modules, followed by team-based reflection and discussion after which the appropriate DfX methods were to be applied within the projects. In the second semester third year students from each team were required to update the class on how these methods had been implemented in their projects.
 Preliminary results show active participation by the fourth year students, with varying levels of application of the tools within the projects. This paper will present specific examples of student led discussion and exercises surrounding DfX topics, as well as a qualitative evaluation of the application of DfX methods by the student teams. Recommendations for future improvement include the development of additional short duration, concrete, formative DfX activities for inclusion in the modules.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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