Üniversite Öğrencilerinin Bireysel Girişimcilik Algıları ile Öğretim Kalitesi Algıları Arasındaki İlişkinin İncelenmesi
Bibliographic record
Abstract
This research aims to investigate the relationship between individual entrepreneurship perception and teaching quality perception of university students. The research was conducted in the relational screening model, which is one of the general screening models, and the group consisted of 241 university students who were eligible to reach the study. In the research, “Individual Entrepreneurship Perception Scale” developed by İncik and Uzun (2017) was used to determine students' entrepreneurship, and “The Scale of assessment of University Students' Teaching Quality” developed by Ginns, Prosser and Barrie (2007) and adapted into Turkish by Özcan (2013) was used to determine the teaching quality. Data collection tools were implemented to the students after being arranged on the web by the researcher. The relationship between individual entrepreneurship perception and teaching quality perception was calculated with Pearson Moment Multiplication Correlation, and changes in terms of other demographic variables were tested with the t-test. According to the results of the research, a positive significant relation was found between the individual entrepreneurship perception and some sub-dimensions of the teaching quality perception. In addition, it was determined that students' individual entrepreneurship perceptions of teaching quality perception did not different by gender.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".