Characteristics and Well Practices About Teaching Learning Process in Graduate Programs According to the Stakeholders
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
This study analyzes some determinants of teaching quality in Master's degree programs in Engineering, taken into account the point of view of students, in a Colombian university, using mixed (quantitative-qualitative) research techniques. The study aggregates factors that are important in such contexts as the institutional environment, theory-practice balance in courses, professor who has experience as researcher, students’ characteristics as well as their previous experiences, and tutoring. These factors are interrelated. In this sense, issues such as professors’ methodology and research experience are highly valued by the students, whereas professors stress the importance of their work as a peer, that is to say being recognized in the academic community as a reference in the discipline. The implications of this research is to know and develop new methodologies to evaluate teacher’s performance but this time in graduate level, topic with fewer evidences than those in undergraduate level.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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