Management Strategies for Enhancing Students’ Entrepreneurship in Industrial and Community Education Colleges of the Northeastern Thailand
Bibliographic record
Abstract
The objectives of this research were to study the needs assessment and to develop management strategies for enhancing the entrepreneurship of students. This study was conducted by utilizing Mixed Methods Research. The sample group used in this study consisted of School Directors and Teachers totaling 226 persons under the Northeastern Industrial and Community Education Colleges in Thailand. The tools used for data collection were questionnaires and strategic evaluation forms. Data were analyzed through descriptive statistics, namely, frequency distributions, percentages, means, standard deviations, and PNI Modified.The results of the research revealed the followings: 1)In all, the current conditions of management for enhancing the entrepreneurship of the students were at a “High” level. Regarding the desired conditions, it was, in general, at the “Highest” level. As for the needs priority, it was found that the most needed aspects were the characteristics of Entrepreneurship, the management of an ‘Entrepreneurial Incubation Center, and Teacher Development, respectively. 2) The management strategies for enhancing the entrepreneurship of students comprised visions, missions, targets, of 9 strategies with 62 measures, and 67 indicators. Some recommendations and suggestions for future research were introduced.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".