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Record W3000895047 · doi:10.24908/pceea.vi0.13720

PROMOTING ENTREPRENEURIAL PRACTICE BY CRAMMING A PRODUCT DEVELOPMENT PROJECT OVER A WEEKEND

2019· article· en· W3000895047 on OpenAlexaffvenue
Kush Bubbar, Dhirendra Shukla

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEntrepreneurshipExperiential learningCohesion (chemistry)WitnessEngineering educationProduct (mathematics)Element (criminal law)SociologyEntrepreneurship educationPedagogyEngineering ethicsEngineeringBusinessEngineering managementComputer sciencePolitical scienceMathematics

Abstract

fetched live from OpenAlex

As the definition of the engineering profession continues to witness a disruptive transformation in the labour market, engineering schools are beginning to recognize the need to facilitate a new approach to education. Such a disruption has lead to the explosion of entrepreneurship education within the boarders of engineering schools in attempt to generate the talent and skills of the future engineer. 
 There is however a gap – engineering schools were never designed to deliver entrepreneurship education. Success in this endeavour relies on a new approach to teaching; one that is founded in a group-based, experiential learning environment. At the University of New Brunswick, entrepreneurship education is a core element offered to all our engineering students.
 The following article proposes the CRAM – a two day hackathon designed to introduce students to a cohesion of contemporary entrepreneurship practices to accelerate group formation and acquire entrepreneurship skill sets using an inductive teaching approach. The delivery of the CRAM is discussed in detail, along with feedback from the student participants.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.881

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 integrity0.0000.000
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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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 designNot applicable
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

Citations3
Published2019
Admission routes2
Has abstractyes

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