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Record W4405675030 · doi:10.24908/pceea.2024.18605

Optimizing Block Model Delivery for Core Engineering Courses: A Faculty Development Journey

2024· article· en· W4405675030 on OpenAlexaffvenueabout
Patrick Molicard-Chartier, Jeff Harris

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsYork University
Fundersnot available
KeywordsCore (optical fiber)Block (permutation group theory)Computer scienceEngineering managementEngineeringMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a case study on the implementation of the Block Model in engineering education, focusing on enhancing student academic experiences and outcomes. It details [Institution's name] School of Engineering's shift from a traditional curriculum to an innovative, project-based Block Model. Highlighting the collaborative efforts between educational developers and faculty, the study reflects on faculty experiences, student feedback, and the impact of this curriculum redesign. Inspired by similar initiatives at other Canadian engineering schools, the paper explores the integration of core components from the Information Use Environment and Experiential Learning Theory into the curriculum redesign, emphasizing a holistic approach to engineering education development. Initial outcomes suggest positive feedback from students and faculty, indicating reduced stress levels and improved engagement. The study concludes with future directions for expanding the Block Model, underscoring its potential in modernizing engineering education to meet contemporary student needs.

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 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 score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.230
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.

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
Published2024
Admission routes3
Has abstractyes

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