Preparation of Higher Education Students in Ecuador: An Analysis Based on the Knowledge Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The knowledge economy has emerged as a key paradigm in global socioeconomic development, highlighting the importance of higher education in the formation of human capital capable of generating, applying and disseminating innovative knowledge. This study aims to evaluate the preparation of higher education students in Ecuador, considering critical variables such as soft skills, perception of the knowledge economy, university-business linkage and internationalization, and their impact on academic training. The methodology employed was quantitative, using a multiple linear regression model to analyze the relationship between the independent variables and the academic formation of a sample of 205 students from two Ecuadorian universities. Advanced statistical techniques were applied to evaluate the significance and impact of each variable. The results indicate that soft skills (r = 0.713, p < 0.01), perception of the knowledge economy (r = 0.602, p < 0.01) and internationalization (r = 0.594, p < 0.01) have a significant and positive impact on academic training. However, university-business linkage showed a lower and non-significant correlation (r = 0.407, p < 0.01). In conclusion, academic training in Ecuador benefits significantly from the development of soft skills, a positive perception of the knowledge economy and internationalization. However, the lack of significant impact of university-business linkages suggests the need for future studies to explore barriers and improve these collaborations. These findings underscore the importance of educational policies that integrate these factors to improve the preparation of students in a global knowledge economy. Received: 16 June 2024 / Accepted: 30 October 2024 / Published: 05 November 2024
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it