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Record W4400864224 · doi:10.1080/14703297.2024.2382413

Embedding entrepreneurship and technology literacy in the student curriculum: A case study of a module for real estate students

2024· article· en· W4400864224 on OpenAlexfundno aff
Matteo Borghi

Bibliographic record

VenueInnovations in Education and Teaching International · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
FundersReal Estate Foundation of British Columbia
KeywordsCurriculumTransformative learningEntrepreneurshipPedagogyExperiential learningLiteracyReal estateSociologyAdaptabilityHigher educationCurriculum developmentMathematics educationPolitical sciencePsychologyManagement

Abstract

fetched live from OpenAlex

Ensuring a pedagogical emphasis on practical entrepreneurial experiences and technology literacy is essential across all educational stages. This paper presents a pioneering case study of curriculum innovation in higher education, specifically within real estate and planning programmes. The ‘Managing Change in the Real Estate Sector’ module is examined as a transformative initiative that integrates entrepreneurship and technology literacy in a subject-specific curriculum. Leveraging experiential learning principles, the module design addresses industry demands and aligns with contemporary educational paradigms. The systemic impact of the module is explored at the student, curriculum, and wider university levels. The study reveals positive outcomes, marked by enhanced student satisfaction, skills development, and industry engagement. The impact extends beyond the classroom, influencing curriculum design and receiving commendation at both internal and external levels. The paper concludes by discussing the broader implications for higher education, emphasising adaptability and innovation.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.504
Teacher spread0.476 · 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 designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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