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Record W2233362105 · doi:10.18260/1-2--11076

Why Not Apply An Engineering Methodology When Creating Courses?

2020· article· en· W2233362105 on OpenAlexaffabout
Josianne Basque, Sylvie Doré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsDictationSession (web analytics)CurriculumComputer scienceSection (typography)Field (mathematics)Mathematics educationMultimediaWorld Wide WebPedagogySociologyPsychologyMathematics

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 2230 Why not apply an engineering methodology when creating courses? Sylvie Doré, Josianne Basque École de technologie supérieure/Télé-université Montréal, Québec, Canada Introduction Much too often, we rely on lectures to teach our students. In times when books were rare and prohibitively expensive, professors in schools and universities would “faire la lecture” or read out loud books at their disposal. In the meantime, under dictation, students would copy the contents of the books on whatever writing material they had at their disposal. At the time, it made sense to lecture, as a basic requirement for learning is having access to the knowledge and it was the only way to do so. Since those days, not only has printing technology evolved, but new media have emerged; understanding of cognitive processes has progressed, learning theories have been developed and tested, new methods and tools have been created. Yet, practices used in most of our engineering faculties and schools do not reflect this wealth of knowledge. One of these practices concerns the way we go about creating a new course or even a new curriculum. This paper presents the concept of instructional engineering (IE), in emergence for the last 40 years in the field of education. The two following sections will attempt to answer the following questions: What is IE? Why use IE? Finally, the last section will quickly present one IE method, namely MISA (a French acronym for Method for engineering learning systems). What is instructional engineering? Simply stated, instructional engineering is a systematic, systemic and heuristic process by which one produces a learning system. Let us first start by examining this process by drawing a parallel with the process used by engineers to create artifacts or products. We will then move on to clarify the concept of learning system. For quite some time, professional engineers have been formalizing the method by which they create products. This has given rise to a large number of design and engineering models. Design is generally considered as an activity by which one generates a set of specifications in order to make a product which will satisfy a given set of requirements and constraints. A design model represents a specific method used to carry out this task. We consider design as a subset of the engineering method in the sense that engineering covers the whole life cycle of a product, starting with the analysis of customer needs, specifications and constraints, moving on to design, production, distribution, maintenance and even recycling. Proceedings of the 2002 Americal Society for Engineering Education Annual Conference & Exposition Copyright © 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0090.008
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.012

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 head0.049
GPT teacher head0.272
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations3
Published2020
Admission routes2
Has abstractyes

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Same topicExperimental Learning in EngineeringFrench-language works237,207