Why Not Apply An Engineering Methodology When Creating Courses?
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.
Bibliographic record
Abstract
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
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it