Elena Athanasopoulou (p), Maria Markou, Aspa Gospodini, Christina Daousi

Marco Delogu

Many Universities are implementing Sustainable Development Plans in line with the 17 Sustainable Development Goals (SDGs) of the UN’s Agenda 2030. It is argued that the preparation of a successful Sustainable Development Plan should involve as much as possible the University students. The students’ perspective is important, not only because they are the largest group in the university, but also because they are the citizens of the future.
This paper presents the findings of a recent research regarding the students' perspective on the SDGs. In particular, the authors have prepared a questionnaire in order to collect important information regarding the students’ knowledge on sustainable development and their attitude and behavior towards the 17 SDGs. After the initial testing and validation, the questionnaire was disseminated to university students and post graduate students in Greece. Among other questions, the students were asked which of the 17 SDGs are the most important for them. The answers were collected online.
Quantitative analysis provides interesting results regarding the most important SDGs for the students, in relation to the field of their studies, sex, age, among other variables. The findings of this research are discussed in comparison to other similar research.

Emanuela Marrocu (p), Raffaele Paci

Elena Athanasopoulou

The paper contributes to the current debate on the assessment of university performance, which is becoming crucial as universities’ financial revenues come from the taxpayers and require a substantial degree of accountability, especially when the public budget constraint is binding. We analyse public universities’ productivity levels in Italy in the years 2010 and 2017, using a two-stage procedure.
In the first stage, we use the nonparametric bootstrap Data Envelopment Analysis method to calculate universities’ internal technical efficiency score in a model with two outputs (teaching and research) and four inputs (students, academic staff, technical staff and financial resources). In the second stage, we employ linear and fractional responses econometric models to evaluate the impact on the internal efficiency generated by the socio-economic characteristics of the region in which the university is located.
First stage results indicate a general increase of relative technical efficiency from 2010 to 2017, coupled with a remarkable reduction in dispersion mainly attributable to Southern universities efficiency improvements. This finding could be interpreted as a signal of the ability of universities to respond to the specific incentives brought about by recent reforms. The second stage provides convincing evidence on how the level of per capita income, students competences and the quality of local institutions affect universities’ efficiency. The paper, by suggesting to decision-makers and practitioners an easy procedure to calculate the universities’ internal efficiency and the impact of contextual factors, offers valuable tools and insights to inform the design of more balanced policy measures to finance the public university system.

Marco Delogu (p), Dimitri Paolini, Raffaele Lagravinese, Giuliano Resce

Emanuela Marrocu

This paper investigates whether Machine Learning methods are valuable tools for predicting students' likelihood of leaving the pursuit of higher education studies. Our analysis takes advantage of administrative data concerning the whole population of Italian students enrolled in bachelor courses for the academic year 2013-2014.
Our numerical results suggest that machine learning algorithms, particularly Random Forest and Gradient Boosting Machines, are potent predictors suggesting their use as early warning indicators. In addition, the feature importance analysis highlights the role of the number of ECTS obtained during the first year for predicting the likelihood of dropout. Accordingly, our analysis suggests that policies aimed to boost the number of ECTS gained in the early academic career may be effective in reducing drop-out rates in Italian universities.