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Record W2886612247 · doi:10.24908/pceea.v0i0.10506

A THREE-TIER EVALUATION RUBRIC FOR THE ASSESSMENT OF GROUP PROJECTS IN CHEMICAL ENGINEERING DESIGN COURSES

2018· article· en· W2886612247 on OpenAlexafffundvenue
Andrew Sowinski, David Taylor

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsRubricGrading (engineering)TeamworkComputer scienceConsistency (knowledge bases)Tier 1 networkGroup workContext (archaeology)Peer assessmentEngineering managementMathematics educationEngineeringPsychologyArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract – Group projects are often key to engineering design courses since they simultaneously develop teamwork and communication skills in the context of solving difficult engineering problems. However, fair, consistent, and transparent grading of these projects are difficult to achieve, and the individual contribution of students can likewise be difficult to evaluate.
 Standardized marking rubrics are often used to increase the consistency and fairness of project evaluations; however, these frequently lack a systematic means for evaluating individual effort within group work. Rubrics also are difficult to employ when there are numerous possible solutions and where some solutions are more elegant or challenging when compared to others.
 To provide a consistent accounting of individual effort and the difficulty of a submitted group design solution, a three-tier marking rubric was developed. Comparing the project grades between two cohorts in the same course showed that there was a broader distribution of grades when using the three-tier marking scheme.

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)0.001
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 score0.250
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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