1,720,980 research outputs found
Campania university students’ motivations to migrate
No full textThe students’ mobility is one specific category of the most general phenomenon called “intellectual migration”, originally called “brain drain”, whose debate dates back to the 1960s, when a quantitatively significant number of qualified people began to emigrate from less wealthy countries towards the richer and more advanced ones.
The paper analyses the Italian internal migration flows of university students, with a focus on students moving from Campania to the rest of Italy to complete their higher education.
With the aim to determine these motivations of such mobility, we use multilevel models to analyze and investigate the key reasons that push university students’ to migrate. We define four macro-determinants:
1) forced-type migration
2) anticipatory migration,
3) prestige migration
4) mobility due to closeness
The empirical analysis is divided into two sections. First one is exploratory analysis, while in the 2nd part multilevel model is performed in an ecological approach, with universities acting as elementary units, and regions as units of upper level. It exploits the natural hierarchical structures of data, with universities nested in regions. Response variable is total number of university students’ from Campania enrolling in other regions. The aim is to test and discuss explanatory variables linked to the 4 working hypotheses, with respect to the school-university transition.
Model findings are will give an hint about real motivations, confirming or disconfirming determinants related to the each of the 4 working hypothesis
Assessing the effects of local contexts on the mobility choices of university students in Campania region in Italy
Full text linkThe mobility of university students in Italy has been framed as a phenomenon linked to so-called intellectual migrations and as a subset of the historical and consolidated internal migration path explained in terms of South–North trajectory. This study describes the most important mobility trajectories of students across macro-areas and disciplinary fields, and then evaluates, using a multilevel logistic regression model, the factors that encouraged student cohort, who were enrolled in a degree program in the academic years 2014–2015, to move elsewhere from the Campania region. Beyond fixed and interaction effects related to the students’ personal characteristics, the model included possible random effects linked to the high schools attended by the students to capture the possible influence of the local context on migration choices
On the determinants of student mobility in an interregional perspective: A focus on campania region
Full text linkThis paper analyses the migration flows of university students from Campania who move to other regions to complete their higher education. The data come from a ministerial student database (Anagrafe M.I.U.R) for the 2006–2007 and 2013–2014 academic years. We first discuss migration from Campania to the rest of Italy to compare other southern regions in the framework in terms of the students’ mobility phenomena. We use a network approach to determine the role of each region and to analyse the global relationships between Italian regions. Multilevel models are then used to analyse and investigate the key reasons for these migratory decisions. We test and discuss (1) forced migration, (2) anticipatory migration, (3) migration influenced by prestige of universities and (4) mobility due to geographic proximity to the place of residence
Combining sentiment analysis and social network analysis to explore twitter opinion spreading
No full textIn this work, we reconstruct the tweet-retweet and tweet-reply relations of opinions about a trending topic on the Twitter platform. We propose a multi-steps approach to derive a signed network expressing the spread of contents and opinions. The first step consists in reducing data dimensionality by means of a clustering procedure on tweets able to identify the concepts they express. In the second step, focusing on message contents, we adapt different sentiment analysis algorithms in order to determine the sign of both the original tweet (with respect to the trending topic) and the sign of the edge connecting the original tweet to the replies, conditional on the replied tweet. Each tweet will spread its concepts by means of signed retweet and reply relations. The aim is to study the different structure, in terms of both network structure and sentiment, of the signed network related to each concept. A comparative analysis will be possible as well among the various identified signed networks
High school proficiency of future university students: an analysis based on INVALSI data
No full textLarge-scale assessment in the education field is key in every Country. In Italy, the institute that is in charge of evaluating pupils’ proficiency is the INVALSI, via a set of standardized tests, that go in parallel with traditional school evaluation. Data collected in a such way at the individual level pose a statistical challenge, given the nested structure of students-classroom-school and the repeated measure longitudinal observations that are obtained for each student. We propose in this context the streamlined version of the mean field variational Bayes (MFVB) algorithm for linear mixed models with crossed random effects, in order to obtain plausible predictors of pupils’ performances. The results and interpretation of model coefficients are in line with the literature on educational data
Determination of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzo-p-furans (PCDFs) and polychlorinated biphenyls (PCBs) in buffalo milk and mozzarella cheese
No full textThe presence of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzo-p-furans (PCDFs) and polychlorinated biphenyls (PCBs) in buffalo milk and mozzarella cheese has been investigated. In total 43 milk samples and 64 mozzarella cheese samples, coming from 40 creameries in the Caserta region of southern Italy, and 26 buffalo mozzarella cheese samples, purchased in Reggio Calabria's supermarkets have been analysed. The extraction and clean up method resulted in an efficient separation of PCDD/Fs and PCBs from other compounds that might interfere with the instrumental analysis. Analysis was carried out using an isotopic dilution method in conjunction with High Resolution Gas Chromatography-High Resolution Mass Spectrometry (HRGC-HRMS). Of the 90 mozzarella cheese samples analysed none exceeded toxicity values higher than the maximum limits requested by CE regulation CE No 2375/2001, 29/11/2001. Five of the 41 milk samples analysed showed toxicity values higher than the maximum law limits
Discovering archetypal universities in higher education mobility flows in Italy
No full textThe aim of this contribution is to identify the archetypal universities in the
Italian students’ mobility network in terms of their attitude in attracting students.
We define a set of networks according to the disciplinary groups by relying upon
administrative data regarding students’ mobility between bachelor’s and master’s
degrees. For each disciplinary group, a network has been defined by considering
the universities as nodes and the flows of students moving between nodes as links.
Then, in each network, the set of archetypal universities is based on several network
centrality indexes. Finally, these archetypes are used as benchmarks to identify the
main determinants of universities’ performances
A Multiplex Network Approach for Analyzing University Students’ Mobility Flows
No full textThis paper proposes a multiplex network approach to analyze the Italian students’ mobility choices from bachelor’s to master’s degrees. We rely upon administrative data on Italian students’ careers by focusing on those who decide to enroll in a different university for their master’s studies once they graduate in a bachelor’s program. These flows are explored by defining a multiplex network approach where the ISCED-F fields of education and training are the layers, the Italian universities are the nodes, and the weighted and directed links measure the number of students moving between nodes. Network centrality measures and layers similarity indexes are computed to highlight the presence of core universities and verify if the network structures are similar across the layers. The results indicate that each field of study is characterized by its network structure, with the most attractive universities usually located in the Center-North of the country. The community detection algorithm highlights that graduates’ mobility between universities is encouraged by the geographical proximity, with different intensities depending on the field of study
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
