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Record W2137947009 · doi:10.24908/pceea.v0i0.3777

THE IMPACT OF A UBIQUITOUS MOBILE COMPUTING ENVIRONMENT ON DESIGN ENGINEERING EDUCATION

2011· article· en· W2137947009 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLaptopSuiteCurriculumComputer scienceComponent (thermodynamics)Engineering educationKey (lock)Mobile deviceEngineering managementUbiquitous computingSoftware engineeringMultimediaEngineeringHuman–computer interactionWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

Providing students with a ubiquitous mobile computing environment is a key component of UOIT’s teaching and learning strategy which offered a unique opportunity to build its programs from the ground-up with a laptop program at its centre. While ensuring that its use was considered in every aspect of curriculum development, five undergraduate engineering programs curricula have been developed at the University’s Faculty of Engineering and Applied Science (FEAS) with a laptop-supported mobile computing environment at its heart. The laptops are equipped with a suite of program specific software. The focus of this paper is on the pedagogical benefits that have been achieved in design engineering education at FEAS as a result of the students’ ubiquitous access to the latest CAD/CAM/CAE and productivity tools. The laptop program enables improved delivery of design engineering training along with the opportunity of implementing novel teaching strategies.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.997

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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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