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Record W2615474054

The Curriculum Innovation Canvas: A Design Thinking Framework for the Engaged Educational Entrepreneur

2017· article· en· W2615474054 on OpenAlexaff
Chelsea R. Willness, Vincent Bruni-Bossio

Bibliographic record

VenueJournal of higher education outreach & engagement/Journal of higher education outreach and engagement. · 2017
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCurriculumExperiential learningVariety (cybernetics)Design thinkingKnowledge managementProcess (computing)PedagogyCurriculum developmentValue (mathematics)SociologyCo-creationEngineering ethicsEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

Integrating literature on entrepreneurial business models and community-based experiential learning, we propose a new framework to advance the practice of curriculum innovation. Grounded in principles of design thinking, the curriculum innovation canvas provides a human-centered, collaborative, and holistic platform for instructors, curriculum developers, and administrators to engage in innovation and implementation of experiential courses or programs—particularly those that involve community or organizational partnerships. The canvas promotes a creative and fluid approach to curriculum development. It prompts the consideration of the value propositions offered to various stakeholders (students, community partners, faculty peers, etc.) as well as how to involve stakeholders in the development and implementation process toward mutually beneficial outcomes in a complex and challenging environment. Evidence from an extensive prototyping process indicates that it can effectively assist instructors, administrators, students, and community partners in a variety of contexts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0040.015
Scholarly communication0.0130.010
Open science0.0030.008
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0070.002

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 head0.038
GPT teacher head0.317
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations29
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of higher education outreach & engagement/Journal of higher education outreach and engagement.Same topicBiomedical and Engineering EducationFrench-language works237,207