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Record W1993658708 · doi:10.1115/detc2010-28167

Introduction to Pre-Robotics: Exploring a Novel Approach for Teaching Mechanical Design

2010· article· en· W1993658708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechatronics Education and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSyllabusMechanical designRoboticsCurriculumProcess (computing)Task (project management)Computer scienceField (mathematics)Engineering design processArtificial intelligenceEducational roboticsDesign methodsRobotEngineering managementEngineeringSystems engineeringMathematics educationMechanical engineeringPedagogyPsychology

Abstract

fetched live from OpenAlex

Educating young engineers in the field of design has always been a challenging task. In particular, teaching some of the aspects of robotics and mechanisms design in a non-mechanical curriculum by far introduces additional challenges. This paper presents an overview of a teaching approach and pedagogical challenges of the author for the past 18 years in teaching (or creating a learning objectives) of the basics of mechanical design methodologies and experiences to sophomore students enrolled in the Engineering Science program. One of the main components of the course syllabus is the notion of design synthesis of a pre-robotic mechanical device. First, the functionality of this device is shown to the students. Next, the students need to propose various design alternatives with mechanical and technical specifications. This paper outlines the method of how the students are guided through the design experience while exploring the basic steps of the design process and specifications.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.658
Threshold uncertainty score0.393

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 integrity0.0000.000
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 head0.056
GPT teacher head0.273
Teacher spread0.216 · 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

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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