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Record W2550875093 · doi:10.18260/1-2--20397

Engineering Students’ Perception of Project-Based Learning Activities at the School of Engineering, UBC Okanagan Campus

2020· article· en· W2550875093 on OpenAlexaff
Claire Yan, Vladan Prodanovic, Ray Taheri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of SaskatchewanOkanagan CollegeOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCapstoneEngineering educationPerceptionWork (physics)Capstone courseProject-based learningEngineeringEngineering managementMathematics educationComputer sciencePsychologyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Junior and Senior Engineering Students’ Perception of Project Based Learning Activities at the School of Engineering, UBC Okanagan CampusThe School of Engineering at UBC Okanagan Campus offers three engineering programs (civil,mechanical and electrical), with the first two years common for all engineering students.Through the course of their education the students are involved in several interdisciplinarydesign projects, including three major design projects in their first and second year as well as theCapstone design projects, a fourth year design course in which students work on industrysponsored real-life projects. In order to evaluate the effectiveness of these project-based learningactivities, and in order to create a better understanding of how students evolve through the courseof their study at the School of Engineering, a targeted survey for the first, second and fourth yearstudents has been designed and conducted. The survey covers students’ views on the structure ofdesign courses, the level of difficulty in relation to other courses, and the build-up of designskills toward the fourth year capstone projects. In this paper the results from the first 2 years ofthe study will be presented and discussed.

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)0.000
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 score0.190
Threshold uncertainty score0.690

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 head0.008
GPT teacher head0.221
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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