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Record W7057448226

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

2014· article· en· W7057448226 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Padua Archive (University of Padua) · 2014
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationChristian ministryContext (archaeology)Promotion (chess)Work (physics)Principal (computer security)Order (exchange)
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.359
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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