MétaCan
Menu
Back to cohort

Supporting Design Thinking Through a Game-Based Pedagogy in Entrepreneurship Education

2020· article· en· W3208012880 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

VenuePapers on postsecondary learning and teaching. · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategies and Innovation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEntrepreneurshipEntrepreneurship educationDesign thinkingMathematics educationPedagogyPsychologySociologyComputer scienceHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

Design thinking is an important concept presented in entrepreneurship education. However, the cognitive aspect of design thinking has been neglected by business teaching and learning practices. The aim of this paper is to present a game-based pedagogy to support the cognitive aspect of design thinking and to promote this approach as an alternative to predictive and adaptive pedagogies that are still dominant in entrepreneurial learning. To disseminate our pedagogical approach, we designed and presented experiential learning activities in a workshop format. In this workshop, the participants took part in ludic tasks such as gameplay and board game design to enhance their comprehension about entrepreneurship through design thinking.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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