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Record W4403794070 · doi:10.24908/pceea.2023.17001

Assessing the Gaps between Alberta K-12 Competency Development and University of Alberta Engineering Introductory-level Graduate Attribute Indicators

2024· article· en· W4403794070 on OpenAlexaffvenueabout
Shuai Yu, Jason P. Carey

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMathematics educationEngineering managementEngineeringPsychology

Abstract

fetched live from OpenAlex

Canadian engineering education is in high demand for students. The University of Alberta (U of A) internal surveys have shown that the first year of engineering program is often the most challenging due to the greater expectations of knowledge and skills, different learning environments, and higher requirements of independent study skills compared to high school. Hence, high school graduates should be equipped with the competencies and knowledge to be more prepared for their first-year engineering studies, which is critical to their success. Therefore, this paper aims first to explore the engineering-specific high school competencies to enter the Faculty of Engineering, U of A. Further, it seeks to identify the gaps between these competencies and the introductory-level graduate attributes (GAs) in the U of A engineering programs. Using a document analysis method, we compared the ENGG introductory-level GAs with the learning outcomes of high school required courses. The findings indicate that high school courses prepare students for first-year engineering courses in terms of knowledge-based and other competencies. However, despite the alignment, gaps also exist in terms of life-long learning, professionalism, ethics and equity, economics and project management that need to be addressed by the program’s first year. Having these skills developed would better prepare students for the first year of engineering and should be a focus of first-year engineering programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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 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

Citations0
Published2024
Admission routes3
Has abstractyes

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