PROMOTING THE ENTREPRENEURSHIP EDUCATION AS CORE COMPETENCE DURING THE BACHELOR STUDIES THROUGH E-LEARNING ENVIRONMENT
Bibliographic record
Abstract
For some countries, the discussion about entrepreneurship, more related to education, seems to be a taboo topic, while for others is a trend which must be valorized. There are many European documents and strategies regarding the promotion of entrepreneurship in the higher education context, from Entrepreneurship in higher education, especially in Non-business studies”, to Lisbon Strategy 2010, Oslo Agenda for Entrepreneurship Education in Europe”, or a newest one Europa 2020 – A European Strategy for smart, sustainable and inclusive growth. Moreover, the economical and social changes we are facing right now couldn’t be anticipated by these documents, but in the same time each of them was developed based on the society’s needs from that time. But one thing is clear: education is one of the most important element which could drive to development, growth and stability. Even when we are talking about employability or nanotechnology, there is or was an educational process in the background which facilitated that. This article aims to present, at the beginning, the core competencies from an European, Australian and Canadian perspective, focused on entrepreneurship education. Both Meyer’s report from 1991 or the European Parliament Recommendation about Key Competencies for Lifelong Learning were focused on entrepreneurship as a competence which must be proved by everybody. The transformations which individuals are facing in the contemporary economy should be anticipated and offered innovative eSolutions in order to develop the necessary competences at work or in day by day life. Higher education institutions could act, in a first phase, as key promoters of this discipline among both business and non-business studies, which will conduct to a great development of mindsets. After a short analysis of how key competences are defined (transversal, core or generic), the author will focus on the actual stadium of teaching entrepreneurship education in Romania and finally he will present some concrete examples of how this topic could be teach using WEB 2.0 technologies: podcast, eMentoring, Moodle platforms or online forums. Also, educational policies and possible solutions are treated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".