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Record W2602049327 · doi:10.24908/pceea.v0i0.6527

PREREQUISITE EVALUATION OF ENGINEERING SKILLS

2017· article· en· W2602049327 on OpenAlexafffundvenueabout
Gérard J. Poitras, Eric Poitras

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversité de Moncton
FundersUniversité de Moncton
KeywordsAccreditationSet (abstract data type)Mathematics educationComputer scienceEngineering educationPsychologyEngineering managementEngineeringMedical educationProgramming language

Abstract

fetched live from OpenAlex

Preliminary findings obtained from a three-year study are presented where different cohorts of undergraduate civil engineering students are followed for three consecutive years while completing the Civil Engineering program at the Université de Moncton. This study outlines how a set of problem instances were developed, wherein a student performs a series of steps to formulate a solution. These steps are mapped to one or more skills, also known as procedural knowledge components, which are essential for students to have mastered from one or more previous courses in order to successfully complete the course in question. Over a hundred students from the second, third, or fourth year performed a series of problem-solving tasks that assess a common set of skills at the beginning of their respected courses. The findings obtained from the first year of this study show that students vary in their abilities to correctly solve instances of a problem on their first attempt. This suggests that there is a pressing need for assessment tools that target progressions for specific courses using the range of standards outlined by the Canadian Engineering Accreditation Board as progress indicators while providing individualized instructional modules developed on the basis of research-based understanding of how these skills develop over time for all students.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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
Published2017
Admission routes4
Has abstractyes

Explore more

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