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Record W2126166457 · doi:10.24908/pceea.v0i0.5860

Triangulated authentic assessment in the HEQCO Learning Outcomes Assessment Consortium

2015· article· en· W2126166457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's University
FundersQueen's University
KeywordsRubricAccreditationStandards-based assessmentMedical educationWork (physics)Lifelong learningEngineering managementQuality (philosophy)Scale (ratio)EngineeringComputer sciencePsychologyKnowledge managementPedagogyEducational assessmentMedicine

Abstract

fetched live from OpenAlex

The Higher Education Quality Council ofOntario (HEQCO) has established a consortium ofinstitutions committed to the development of usefullearning outcomes assessment techniques and to theirwide-scale implementation in their institutions. Queen'sUniversity is one of three universities and three collegesof the consortium, and the Faculty of Engineering andApplied Science (FEAS) is participating due to familiaritywith assessing learning outcomes as part of accreditation.The specific learning outcomes that are of interest toQueen's are Critical Thinking, Problem Solving,Communication and Lifelong learning.The goal of this three-year project is to assess theaforementioned general learning outcomes and cognitiveskills using three assessment methods simultaneously:embedded course assessment, using meta--rubrics toscore student artifacts, and using standardizedtests/surveys. The study will document cost and timerequired to access each of these methods in specificcourses, analyze correlation between scores from thethree methods, and evaluate developments of the genericlearning outcomes over the duration of a program. Weaim to ensure that the work of outcomes assessment issustainable, works within standard course contexts, andcan be integrated into regular course activities. Thepaper identifies the goals of the project, currentapproach, and an example of data collection in one firstyearengineering design course.

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.

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)0.001
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 score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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