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Record W2903779322 · doi:10.21606/drs.2018.578

Using Design Competencies to Define Curricula and Support Learners

2018· article· en· W2903779322 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.

Bibliographic record

VenueProceedings of DRS · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsOntario College of Art and DesignBeef Farmers of Ontario
Fundersnot available
KeywordsCurriculumComputer scienceSet (abstract data type)Core competencyDesign elements and principlesDesign educationVisualizationMathematics educationKnowledge managementSoftware engineeringPsychologyPedagogyArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

This paper presents findings from design research related to a Design Competency Framework (DCF). The DCF is a visually-oriented system for developing curricula in design and is an example of the application of design research to design education. The DCF is divided into a set of sixteen categories including core skills, such as visualisation, and meta competencies such as synthesis. These are presented in the form of a matrix. We see three distinct advantages of using such a system. Firstly the DCF is personalisable at various scales such as individuals, units, courses, and programs. Secondly it is student centred - while we do not assume that design students are passive consumers of their own curricula in non-competency based design education we make the case here for student access to curriculum design processes. The DCF allows students to participate in the design of their own education. Finally, the DCF is resistant to imposition from above and as such questions the modes and institutional dynamics through which design courses come into being.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.292
Teacher spread0.233 · 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