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Record W4294898282 · doi:10.34190/ecie.17.1.652

Enabling Undergraduate Student Entrepreneurs to Structure Their own Experiential Learning Course

2022· article· en· W4294898282 on OpenAlexaff
Kenneth A. Grant

Bibliographic record

VenueEuropean Conference on Innovation and Entrepreneurship · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEntrepreneurshipExperiential learningMentorshipCourse (navigation)Personal developmentPsychologyMathematics educationPedagogyWork (physics)Medical educationEngineeringPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The one-semester undergraduate course “Applied Entrepreneurship” allows student entrepreneurs to investigate their own business idea while following a course structure that they design for themselves under the mentorship of an entrepreneurship professor. The course is available to all students across campus, regardless of degree program or year of study. No prior courses in entrepreneurship are required (indeed the only students excluded are those already enrolled in the Management School Entrepreneurship Major). The course is based on a widely held teaching principle for entrepreneurship education -- getting the students out of the classroom and into the real world. Students bring their own business idea to the course, which can be at any stage of development from ideation, through launch, operation and even sale of their business. Each student develops a personal course workplan of eight modules including a mix of structured learning and primary and secondary research appropriate for their own personal development as entrepreneurs and about their business idea. A short write-up is prepared by the student for each module, including evidence of the work they have done. They conclude the course by presenting a summary of the work they have done and a personal reflection. As the course progresses, the instructor provides individual feedback and counselling to each student as they submit their modules. Student feedback on the course is highly positive, on the education experience, improvement of their business idea and on their personal development.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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.

Study designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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