Identification of Lifelong Learning Characteristics and Entrepreneurial Motivation among Students in a Public University
Bibliographic record
Abstract
Lifelong learning is an important factor in raising the level of knowledge and skills and improving the quality of life. For entrepreneurs, lifelong learning is the basis of long-term success because it encourages them to become open, creative and innovative leaders. Despite being said to be the solution to the narrowness of future job availability, entrepreneurship is still did not get enough attention from the students, whom are the future worker themselves. Past researches had reported that the students are lack of motivation and research related to the motivation factors also said to be scarce. On the other hand, studies regarding lifelong learning in education system had indicated several issues including lack of implementation of lifelong learning including level of student, low usage of teaching approach towards lifelong learning and lack in skills related to lifelong learning. Therefore, this study focuses on undergraduate students and aims to understand their entrepreneurial motivation in light of lifelong learning or whether their motivation is influenced by it. This study had adopted a quantitative research method. Total of 393 Bachelor Degree students from a public university in Malaysia had involved as the respondents selected through random sampling method. Data analysis was done using descriptive analysis and multiple linear regression. After the analysis, it shows that lifelong learning characteristics and lifelong learning tendencies were both able to explain 14.4% of the variation of entrepreneurial motivation and statistically significant (R2 = 0.144; F (2, 390) = 32.88, p = < .000). It means that the independent variable is statistically significant in predicting the dependent variable. Lastly, it is in the hope that this research can be a guide for the nations in producing a quality graduates that can become successful entrepreneur.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".