Crafting the Effective Learning Model for High Education: An Investigation from the business Incubator in Three Universities in Indonesia
Bibliographic record
Abstract
The study aims to examine the current implementation of entrepreneurship learning including business incubator and to identify factors influencing the effectiveness of entrepreneurship and incubator output including developing collaborative learning model in each particular faculties/university in Jakarta and Tangerang. Method of the study uses qualitative analysis which is focusing on Focus Group Discussion (FGD) and in -depth interview approaches to eight universities and fifteen academicians with strong background in managing as well as entrepreneurship teaching experiences. The result shows that strong collaboration between the study programs and business incubator must be strongly established to provide entrepreneurship learning experiences in higher education. There are significant aspects as influencing factors to create collaborative model between entrepreneurship program and business incubator; university’s vision and mission, entrepreneurial background lecturer, strong culture and reward system. This study is claimed as few studies focusing on developing a collaborative model specifically to the universities which have business incubator in Indonesia. Studies on the effect on collaborative learning model on entrepreneurship toward succesful entrepreneurship education would be considerably fill in the gaps in the body of knowledge on the subject.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".