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Record W2418676767 · doi:10.18260/1-2--11220

Animations As Support For The Teaching Of The Manufacturing

2020· article· en· W2418676767 on OpenAlex
Marek Balazinski, Aleksander Przybyło

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceAnimationFlash (photography)Session (web analytics)Process (computing)Variety (cybernetics)SoftwareMultimediaWorld Wide WebArtificial intelligenceComputer graphics (images)Programming language

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Main Menu Session 3263 Animations as support for the teaching of manufacturing Marek Balazinski, Aleksander Przybylo École Polytechnique de Montréal, Mechanical Engineering Department Abstract In this paper a variety of computer animations are presented. These animations are used during the Advanced Manufacturing course given in Mechanical Engineering Department at École Polytechnique de Montréal. The project has been realized using the Macromedias Flash 5 and Corel Draw 10 software. The teaching evaluation of the animations as a lecture tool proved that this new learning technology produces excellent results and enhances the teaching process. Key words: teaching, manufacturing, simulation, animation. 1 Introduction Teaching manufacturing processes requires students to acquire a good understanding of theories related to strength of materials, heat transfer, materials structure, etc. Manufacturing processes are often very complex and difficult to explain; therefore, the implementation of numerous laboratory sessions is required. Laboratory sessions are expensive, long to prepare and their efficiency is sometimes affected by parasitic phenomena that make the interpretation of laboratory results difficult. The use of films is also long and costly. In addition, films make it impossible to separate the different phenomena that come into play in a manufacturing process. Computer animations and simulations more easily show the individual process of interest. Several studies [1, 2] find multimedia instruction both more effective and more efficient than conventional instruction. Recently developed software libraries and tools such as Macromedia Flash 5 TM make the development of animations and simulations possible, even though they are not specially developed for this purpose. During the Advanced Manufacturing course given in the Mechanical Engineering Department at École Polytechnique de Montréal, in order to explain some manufacturing problems, a variety of computer animations have been realized. The slide-shows used during this course (which were already containing short movies) have been enhanced with animations available through the course web site at the address: http://www.cours.polymtl.ca/mec4530/Anim/Menu.swf Proceedings of the 2002 American Society for Engineering Education Annual Conference &Exposition Copyright Ó 2002, American Society for Engineering Education Main Menu

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.164

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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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