La valutazione dell'efficacia interna di corsi universitari: l'impatto del contesto e dei legami relazionali tra gli studenti

L'obiettivo di questo lavoro è quello di fornire una panoramica su come includere gli effetti di contesto e dei legami relazionali tra gli studenti in un modello per la valutazione dell'efficacia interna di corsi universitari. In particolare, a seconda della tipologia dei dati, possono essere utilizzati diversi strumenti metodologici: un approccio basatu sui peer effects, che sviluppa l'assunzione che i comportamenti degli studenti sono influenzati da quelli dei loro pari, e un approccio basato sull'introduzione di variabili individuali legate al comportamento ed alle capacità relazionali degli studenti. Dopo una breve introduzione a questi tipi di approcci, vengono presentati i risultati di alcune applicazioni empiriche condotte sugli studenti di un Corso di laurea magistrale dell'università di Roma “Tor Vergata”. L'evidenza empirica suggerisce che sia gli effetti di contesto, sia le caratteristiche relazionali degli studenti sono predittori significativi del rendimento universitario individuale.This paper is aimed at providing an overview on how to include contextual effects and relational ties between students in a model for the evaluation of the internal effectiveness of a degree course. In particular, depending on the type of available data, different methods are proposed: an approach based on peer effects, that develops the assumption that the behaviors of students are influenced by those of their peers; and an approach based on the introduction of individual variables, related to the student relational behaviors and abilities. After a brief introduction to these techniques, we present the results of some empirical applications, conducted on the students of a second level degree course of the university of Rome Tor Vergata. Empirical evidence suggests that both contextual effects and student relational features are significant predictors of individual academic achievement

Consumer attitudes and preference exploration towards fresh-cut salads using best–worst scaling and latent class analysis

This research explored the preferences and buying habits of a sample of 620 consumers of fresh-cut, ready-to-eat salads. A best–worst scaling approach was used to measure the level of preference stated by individuals regarding 12 attributes for quality (intrinsic, extrinsic and credence) of fresh-cut salads. The experiment was carried out through direct interviews at several large-scale retail outlets in the Turin metropolitan area (north-west of Italy). Out of the total number of questioned consumers, 35% said they did not consume fresh-cut salads. On the contrary, the rest of the involved sample expressed the highest degree of preference towards the freshness/appearance attribute, followed by the expiration date and the brand. On the contrary, attributes such as price, organic certification and food safety did not emerge as discriminating factors in consumer choices. Additionally, five clusters of consumers were identified, whose preferences are related both to purchasing styles and socio-demographic variables. In conclusion, this research has highlighted the positive attitude of consumers towards quality products backed by a brand, providing ideas for companies to improve within this sector and implement strategies to answer the needs of a new segment of consumers, by determining market opportunities that aim to strengthen local brands

A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision

Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales (i.e., multi-scale, MS) in an efficient and optimized (in terms of trainable parameters) way, and was validated on three P300 datasets. The CNN was trained using different strategies (within-participant and within-session, within-participant and cross-session, leave-one-subject-out, transfer learning) and was compared with several state-of-the-art (SOA) algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs

Extraction of acoustic sources for multiple arrays based on the ray space transform

In this paper we present a source extraction technique for multiple uniform linear arrays distributed in space. The technique adopts the Ray Space Transform representation of the sound field, which is inherently based on the Plane Wave Decomposition. The Ray Space Transform gives us an intuitive representation of the acoustic field, thus enabling the adoption of geometrically-motivated constraints in the spatial filter design. The proposed approach is semi-blind since it needs as input an estimate of the source positions. We prove the effectiveness of the proposed solution through simulations using both white noise and speech signals

The magnetic fields of hot subdwarf stars

Detection of magnetic fields has been reported in several sdO and sdB stars. Recent literature has cast doubts on the reliability of most of these detections. We revisit data previously published in the literature, and we present new observations to clarify the question of how common magnetic fields are in subdwarf stars. We consider a sample of about 40 hot subdwarf stars. About 30 of them have been observed with the FORS1 and FORS2 instruments of the ESO VLT. Here we present new FORS1 field measurements for 17 stars, 14 of which have never been observed for magnetic fields before. We also critically review the measurements already published in the literature, and in particular we try to explain why previous papers based on the same FORS1 data have reported contradictory results. All new and re-reduced measurements obtained with FORS1 are shown to be consistent with non-detection of magnetic fields. We explain previous spurious field detections from data obtained with FORS1 as due to a non-optimal method of wavelength calibration. Field detections in other surveys are found to be uncertain or doubtful, and certainly in need of confirmation. There is presently no strong evidence for the occurrence of a magnetic field in any sdB or sdO star, with typical longitudinal field uncertainties of the order of 2-400 G. It appears that globally simple fields of more than about 1 or 2 kG in strength occur in at most a few percent of hot subdwarfs, and may be completely absent at this strength. Further high-precision surveys, both with high-resolution spectropolarimeters and with instruments similar to FORS1 on large telescopes, would be very valuable

Animal welfare and gender: a nexus in awareness and preference when choosing fresh beef meat?

The modern consumer is now more attentive towards animal welfare practices and this represents an important factor when purchasing meat, whereby ethical, sociological and economic implications are evaluated. In addition, the socio-demographic characteristics of consumers evidence different sensitivities with regard to selection patterns and consumption styles. This study aims to explore the role of Gender in beef meat purchasing preferences, assessing consumer awareness of responsibility towards animal welfare, through the use of cross-tabulation with χ2 to test the different behaviour of men and women and the use of principal component analysis and cluster analysis to classify attitudes of choice according to gender. Among the research aims, this study examined consumer attitudes towards certain 'ethically incorrect' animal products, as well as their awareness of the institutional responsibility in controlling animal welfare standards during the meat production process. The study conducted in Northwest Italy, involving 512 respondents, shows that women are more sensitive to AW aspects and place trust in those responsible for certification of animal welfare standards, such as veterinarians and consumer associations, and also shows that it is possible to identify an 'animal welfare sensitive' profile of meat consumer.HIGHLIGHTS Modern consumer evaluates ethical, sociological and economic implications in animal friendly meat purchasing process Gender affects awareness of the responsibilities of veterinary, public health control bodies and consumer associations to verify animal welfare Cluster highlighted consumer differences in perception towards animal welfar