Investigating teaching and learning methods in Italian universities and beyond. The quest to improve and share practices and strategies in the international higher education context
Bibliographic record
Abstract
The present paper aims to describe the first phase (10 months) of an Italian research unit, which is part of a more extensive three year project that endeavored to: design innovative programs for higher education, to promote personalized learning, to build on job competencies, to value talents, to create new work opportunities, and to provide positive strategies in higher education to support young adults during their employment emergency as a response to the socio-economic crisis and as a citizenship action. In response, a consortium of universities and researchers, called Emp&Co (Employability and Competences) was created and funded from the Ministry of Research and University. The project involves six Italian Universities (Padova, Firenze, Siena, Napoli Parthenope, Molise, Roma Sapienza), and Dr. Monica Fedeli from the University of Padova is the Principal Investigator. Dr. Fedeli’s research group is focusing on the innovation of teaching and learning methods and promotion of personalized programs in order to modernize university didactics, to encourage the university-business dialogue, and to promote employability. In the first phase of the project, a literature review was conducted and an analysis of student evaluation questionnaires on didactics, from 8 Italian, 3 American, 5 European, 1 Canadian, and 1 African University was completed to compare and learn from the different ways in which courses are evaluated by students in the different academic contexts. The goal at the end of the first year is the creation of a questionnaire template focused on teaching and learning methods, in order to gain a better understanding of student perspectives in our country. Additionally, we hope to implement strategies that will improve university didactics as requested by all European Union declarations. This paper introduces the overall study and the foregrounding work done with Dr. Joellen Coryell from Texas State University in the United States, who is a consultant to the University of Padova in the areas of adult and higher education teaching and learning.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".