1,721,033 research outputs found
La scuola è uguale per tutti? Alcune riflessioni sull’istruzione in Italia a 55 anni dalla Scuola di Barbiana.
No full textIl lavoro percorre le tappe dell'istruzione italiana dal dopoguerra ad oggi puntando l'attenzione sulle diseguaglianze presenti nella scuola secondaria e all'universita
I grandi numeri dell'istruzione secondaria e terziaria
No full textThis works deals with the “big” changes occurred in the upper secondary (high school) and tertiary education in Italy in the last 20 years. It can be divided into three steps: 1. High school, 2. High school-university transition; 3. University enrollment and careers.
High school. With reference to upper secondary education, the educational attendance and attainment have reached very good results in the last 60 years, as 90% of the young students get nowadays a high school diploma with some differences in favor of the northern-central regions of the country. Moreover, there have been profound changes in the choices relating to the type of high school: a decrease of the students attending the “Liceo classico” (with more females); an increase in the “Liceo Scientifico” (males and females are equal just in the last years) and in the “Istituti Tecnici”, attended mostly by males. These Istituti are more frequent in the North of Italy because they are linked to the labour market.
High school-university transition. In the last 60 years, the transition rates from school to university is up and down with a peak in the 90s and in the last years rates are around 50-60%. On the other hand, the number of university students has steadily increased in the 70s and 80s, as the baby boomers were more numerous and more educated than their previous generations. In the last years, there rates have not increased probably due to the economic crisis.
University enrollment and careers. In the 1989/90 female freshmen overcome male freshmen and the distance between females-males have increased in the last 10 years (the total number of freshmen is around 280,000 in the last 10 years).
Performance is better for females students in the Bachelor’s degree courses and it enlarges the differences between males: 27.6 females out of 100 drop out in the 5 years of observation (cohort of freshmen of 2014/15), compared to 37.3 out of 100 males, denoting as males have a greater propensity to leave. On the other hand, observing the transition to the Master, we have noticed - unexpectedly - an inversion, in fact, female students enrolled in the Master within 4 years are 57% (32.6% / (32.6% + 24.6%)) while males enrolled are the 66.5% (29.6% / (29.6% + 14.9%)). This change (keeping in mind that in the master courses females are still 20,000 more numerous than males) is particularly interesting and certainly deserves further studies. Moreover, the school of medicine is very interesting too because females have surpassed males in the number of freshmen in the last 8 years with a similar performance between the two groups. Other “very Italian” issues are considered, as there are still a lot of differences between North and South of Italy, between students coming from “Liceo Classico/scientifico” and students coming from other type of schools, between success in the scientific field and other fields
Admission Test to University in Italy: A Performance Comparison of Regression Models for TOLC-S
No full textIn Italy, the transition from secondary school to university is a critical phase, with approximately 20% of students experiencing dropout or course changes. Admission tests, such as the TOLC-S, aim to assess students’ preparation and support informed decision-making for both students and institutions. This study investigates the predictive power of the TOLC-S in forecasting university success, measured by the number of credits earned in the first year. To facilitate the analysis, a matching procedure was implemented to merge the CISIA admission test database with the National Student Archive (ANS), overcoming the absence of a common student identifier through a derived key. The study compares three regression models: a Generalized Linear Mixed Model (GLMM) with the university as a random effect, an Elastic Net, and a Random Forest. Model performance was evaluated in terms of accuracy, implementing a repeated 10-fold cross-validation to get more robust results. The results offer insights into the extent to which admission test scores influence long-term academic outcomes, contributing to the improvement of university predictive models and informing policy decisions on student selection and support
Gender differences in career advancements in Italian universities over the last 20 years
Full text linkAbstract This article deals with gender differences in Italian universities in the last 20 years in terms of career advancements. Data are taken from the MUR (Ministry of University and Research) archive. In Italy, career advancements are still much easier for men, even if the gender gap has slowly narrowed in the last decades. The novelty of this paper is the analysis through event-history analysis models on the time elapsed to receive a promotion (from assistant to associate professor and from associate to full professor). The event-history analysis applied to career advancements has revealed that women take, on average, about one and a half more years than men to advance, with some differences among fields of study and macroregions. Furthermore, this gender gap is higher in the first years of the career. Two sociological metaphors used in the gender literature, the “leaky pipeline” and the “glass ceiling”, seem to intervene powerfully in the gender gap of Italian universities careers
Moving from North to North: how are the students’ university flows?
Full text linkAbstract Student mobility has been much commented upon and much studied. Student mobility has social, economic, and political consequences. This form of mobility is relevant, in Italy, in terms of south-north flows, while the mobility of northern students toward the South and Centre of Italy is negligible. To the best of our knowledge, a proper focus on the dynamics among northern regions has not yet been carried out. This study focuses on the interregional mobility of northern first-year students. To this end, we use a longitudinal dataset with students’ individual histories from 2008 to 2017, obtained from the cohort-based datasets collected using the Italian Ministry of University’s administrative databases. Descriptive and model-based analyses are employed for assessing the association between the propensity to move and individual characteristics, as well as some territorial variables. A longitudinal study is also considered. Here, we see an increase in the population entering the university system and mobility flows across northern regions. The results show that students’ educational experiences influence the propensity to move. However, the most relevant driver of the phenomenon is the attractiveness of areas with a higher supply of university courses and a better economic context
The Predictivity of Access Tests for University Success
No full textIn Italy, the transition from secondary school to university is a topic of debate. The transition from the first to the second year is particularly crucial, with dropout and degree course changes around 20%, with many attributed to a lack of information and guidance. Admission tests play an important role during this transition, as they should provide information for both the student and university. This chapter analyzes the predictivity of admission, with respect to the credits earned at the end of the first year. To achieve this goal, a “matching” algorithm was developed, merging the admission test database provided by CISIA and the National Student Archive of the Ministry of Education (MIUR). The CISIA database contains personal information, test scores, and the date and location of the test, while the MIUR dataset contains personal and career information for all students enrolled in any Italian university. Neither database contains a common primary key of the student. To overcome this limitation, a derived key was constructed by concatenating variables from both datasets. In this way, a mixed logit was applied to detect the students’ profiles with higher probability of earning more than 20 and 40 credits at the end of first year
Does taking additional Maths classes in high school affect academic outcomes?
Full text linkSeveral studies in the mathematical education literature show the effect of students’ high school skills in maths on their success at higher levels of education and work. In particular, the importance of maths course taking in US high schools is highlighted to be important for college enrolment and completion. The choice of taking additional maths courses or, as in Italy, of choosing a high-school curriculum with more maths, is not random: it depends on several substantial factors such as gender and socio-economic status. This selection bias implies that the differences in the academic outcomes might be traceable not only to mathematics ability and knowledge. In this paper, the aim is to estimate the treatment effect of attending a relatively new high school curriculum in Italy with more maths, with respect to the traditional track of the scientific “liceo”, on two academic outcomes: university enrolment and first-year university performance. After having reduced the selection bias using a caliper multi-level propensity score matching procedure, a multi-state Markov model is used to study the treatment effect on the joint educational outcomes
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