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Record W4387759073 · doi:10.24908/pceea.2020.16837

ADVANCED GRAPHICAL COMMUNICATIONS – A COURSE EVOLUTION

2020· article· en· W4387759073 on OpenAlexaffvenueabout
James A. Sykes

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCourse (navigation)Class (philosophy)Geometric dimensioning and tolerancingSelection (genetic algorithm)DimensioningComputer scienceCore (optical fiber)EngineeringEngineering drawingArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

After a new course is introduced, its content and structure evolve to a level of relative stability. Part of that evolution is the recognition and redressing of knowledge gaps in the student body. This paper will recount the introduction and evolution of Advanced Graphical Communication (AGC), a senior-level mechanical engineering technical elective introduced in 2016 at the University of Manitoba. Geometric Dimensioning and Tolerancing (GD&T) is the core of AGC, supported by the development of drawing creation and drawing checking skillsets. Created at the request of local industry to address a knowledge gap in graduates, industry also partners with the AGC course, placing employees in the class along with undergraduate students. As the course evolved over 4 sessions, assignments were changed or modified, and support materials for various design considerations were developed. Throughout the course evolution, gaps in the students’ foundational knowledge became evident; core knowledge of conventional manufacturing processes and how to select appropriate materials for a design, for example, were absent. The instructor also identified that design esoterica, such as surface finish and fit selection that are critical to a complete design specification, were not addressed in their previous studies. This paper will recount how AGC evolved and how it addressed some of the gaps using instructor-supported focused modules.Beyond this specific course, however, such modules could be expanded to independent micro-courses (IμC, pronounced eye-mu-cee). Specific design knowledge and skillsets will inevitably be missing in an engineering faculty, resulting in lost learning opportunities for students. IμCs are envisioned as engineering design content accessible to the student on demand, allowing discrete learning opportunities to be incorporated as a component in a course or accessed for co-curricular design competitions and capstone projects. These modules would ideally be independent of instructor support and may include physical artifacts that demonstrate specific elements within the module. Whereas conventional teaching pushes the content on the student, IμCs allow pull-based content delivery, fostering students’ ownership of their learning.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.730

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 science0.0010.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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