Students’ Perspectives on Significant and Ideal Learning Experiences —A Challenge for the Professional Development of University Teachers
Bibliographic record
Abstract
The paper presents a study of students’ significant and ideal learning experiences as triggers of university teachers’ professional development. Students’ feedback and teachers’ own reflection on their teaching act as important triggers for quality shifts in their teaching and professional development. The results of empirical research, during which we used a questionnaire with predominantly open-ended questions, will be presented. We were interested in the degree of students’ satisfaction with the quality of education and what conceptions about teacher’s and student’s role they had formed during their studies. Of the many research questions, this paper only deals with analysis of learning experiences which had a particular impact on students, and their notion of an ideal study environment. In this manner we attempted to reflect on the quality of studying and, based on significant learning situations, gain insight into the influence a teacher’s teaching may have on their students’ professional and personal development. Thus the question arises of how much university teachers are prepared for in-depth reflection on their own practices, to what degree they are ready to take into account feedback they receive from students and whether they are prepared to abandon their customary teaching practices.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".