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Record W1957721186 · doi:10.24908/pceea.v0i0.5839

USE OF EPORTFOLIO TOOL FOR REFLECTION IN ENGINEERING DESIGN

2015· article· en· W1957721186 on OpenAlexaffvenueabout
Ryan Clemmer, Jennifer C. Spencer, Dale Lackeyram, Jason Thompson, Bahram Gharabaghi, Jonathan VanderSteen, John Donald, Richard G. Zytner

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDeliverableDocumentationRubricTeamworkReflection (computer programming)Mathematics educationPsychologyEngineering educationSoft skillsLifelong learningReflective practiceMedical educationComputer sciencePedagogyEngineeringEngineering managementSystems engineeringMedicine

Abstract

fetched live from OpenAlex

Electronic portfolios (ePortfolios) can be a beneficial tool to facilitate student learning, evaluate learning outcomes and showcase skills and experience. At the University of Guelph, the School of Engineering piloted the use of ePortfolios within the third year design course of the engineering design sequence of courses. With the implementation of graduate attributes by the CEAB, more “soft skill” attributes like individual and teamwork, project management, and lifelong learning are important skills developed by students within the design courses and can be assessed within an ePortfolio environment.Students submitted guided reflections related to major deliverables within the course. The reflections were assessed for the level of insight through rubrics in the learning management system. Overall, students improved their ability to reflect and provided good insight into their learning and roles within their group project. The response to the reflections by students was mixed. Many students found value in reflecting on their experience while other students were frustrated by the method of filling the reflection form.In the future, the objectives for reflection should be made clearer with supplementary documentation to the lecture material. Adjusting the timing of the reflections to correspond to less stressful periods of the semester and improving the ePortfolio process will help with student engagement.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.061
GPT teacher head0.326
Teacher spread0.266 · 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 designObservational
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

Citations6
Published2015
Admission routes3
Has abstractyes

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