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Record W2885022454 · doi:10.24908/pceea.v0i0.10371

Effective Coordination of Capstone Design Work Using Milestones

2018· article· en· W2885022454 on OpenAlexafffundvenue
Michel F. Couturier, Guida Bendrich, Francis Lang

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMilestoneCapstoneDeliverablePaceWork (physics)Engineering managementQuality (philosophy)EngineeringComputer scienceSystems engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract – A universal management framework has been developed for coordinating the work of students and mentors in the capstone design course offered in the Chemical Engineering program at the University of New Brunswick. The framework makes use of seven evenly spaced milestones to pace the students through their design project. The milestone documents describe the main tasks to be completed by students and apply to any client-based project. They also provide the marking scheme to be used by mentors when evaluating the team reports submitted at the end of each milestone. As an added benefit, the universal milestone framework also enables the progressive assembly of a high-quality final report since each milestone report is tailored to be a section of the final report. As expected, the performance of students on the final report is generally better than the average of their milestone grades. Furthermore, the universal framework ensures deliverables are consistent for every group regardless of their project, which results in a streamlined experience for both students and instructors. Student opinion surveys suggest that students appreciate the frequency of the milestones, which allows for continuous feedback and appropriate pacing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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 designObservational
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
Published2018
Admission routes3
Has abstractyes

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