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Record W3003105828 · doi:10.24908/pceea.vi0.13708

Arduino-based Sensor Device – An Engineering Physics Second-year Design Project within the Engineering Design and Practice Sequence (EDPS) of the Queen’s Engineering Professional Spine

2019· article· en· W3003105828 on OpenAlexaffvenueabout
R. Knobel, Mark Chen, L. Clapham

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccreditationEngineering design processScope (computer science)EngineeringEngineering educationProcess (computing)Engineering managementEngineering ethicsMedical educationComputer scienceMedicineMechanical engineering

Abstract

fetched live from OpenAlex

In 2011, Queen’s Engineering began rollout of its "Engineering Design and Practice Sequence (EDPS)". The EDPS is a "professional spine" sequence of courses over four years, meant to address and incorporate into all of its engineering programs the majority of the 12 Graduate attributes required by the Canadian Engineering Accreditation Board (CEAB). In year 1, the first EDPS course – Engineering Practice I - introduces students to engineering design and problem solving, but with little formal instruction in the design process and engineering tools. Formal instruction in these aspects comes in second year, in Engineering Design and Practice II (course number APSC200). Finally, in third and fourth year, students undertake significant design projects in their discipline. 
 The second-year version of the professional spine, APSC200, is a one-term course taken by all students. This begins with a 6-week Faculty-wide course module, followed by a 6-week program-specific module. In the first Faculty-wide segment, students learn the design process – problem definition and scope, idea generation and broadening tools, decision-making tools, economic analysis, stakeholders, risk, and safety. Students are exposed to the necessity of formal design techniques via a zero-level "P0" project, and taught these techniques during a more extensive P1 project.
 The second 6 weeks of APSC200 involves a discipline-specific project (P2) in which the student teams practice the skills introduced in the earlier portion of the course while working through a design project chosen to emphasize the skills of their program. This paper focusses on the development and implementation of the P2 project for students in the Queen’s Engineering Physics program. The goal of this project is to introduce discipline-specific tools and techniques, to excite students in their chosen engineering discipline, and to put into practice the formal design techniques introduced earlier.
 The P2 project developed for Engineering Physics was entitled a "Compact Environmental Monitoring Station". The premise was that the Ontario Ministry of the Environment (MOE) issued an RFP for small, cheap sensor devices that could be provided to every Ontario household, and set up to "crowdsource" environmental data for the MOE. Student teams were required to research and justify which environmental parameters would be appropriate for their monitoring device, decide on parameters to monitor, design the device, and build a working prototype of the device.
 The device specifications required the use of an Arduino-based platform, interfacing the chosen sensor(s) to a laptop computer using MatLab. Since only some students were familiar with Arduinos and MatLab, two "just in time" workshops were delivered on these topics, using a "flipped lab" approach.
 For the prototype design and build, students had only 4 weeks and a budget of $100. Arduino boards and some basic sensors were supplied, with students able to source and purchase other components within their budget. The prototype-build provided the students with a valuable hands-on experience and also helped them to fully appreciate unexpected practical design constraints. Given the short timeframe (4-weeks) for the design and build, prototypes were very impressive, with many including solar power or rechargeable batteries, Bluetooth connectivity, 3-D printed packaging, IPhone or Android apps, as well as calibration functions.
 This paper will summarize the development of this Engineering Physics P2 module, and will report on the first year of offering it in its current format.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.238
Teacher spread0.225 · 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.

Study designSimulation or modeling
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".

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Citations0
Published2019
Admission routes3
Has abstractyes

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