Contribution to book
Analysis and visualization of performance indicators in university admission tests
Natilli M., Fadda D., Rinzivillo S., Pedreschi D., Licari F.
This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the ques- tion or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the compo- sition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.Source: Formal Methods. FM 2019 International Workshops, edited by Emil Sekerinski et al..., pp. 186–199, 2019
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A visual analytics platform to measure performance on university entrance tests
Boncoraglio D., Deri F., Distefano F., Fadda D., Filippi G., Forte G., Licari F., Natilli M., Pedreschi D., Rinzivillo S.
Data visualization dashboards provide an efficient approach that helps to improve the ability to understand the information behind complex databases. It is possible with such tools to create new insights, to represent keys indicators of the activity, to communicate (in real-time) snapshots of the state of the work. In this paper, we present a visual analytics platform created for the exploration and analysis of performance data on entrance tests taken by Italian students when entering the university career. The data is provided by CISIA (Consorzio Interuniversitario Sistemi Integrati per l'Accesso), a non-profit consortium formed exclusively by public universities. With this platform, it is possible to explore the performance of the students along different dimensions, such as gender, high school of provenience, type of test and so on.Source: 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia, Grosseto, Italy (Grosseto), Italy, 16-19 June 2019
ceur-ws.org | ISTI Repository | CNR ExploRA
Exploring students eating habits through individual profiling and clustering analysis
Natilli M., Monreale A., Guidotti R., Pappalardo L.
Individual well-being strongly depends on food habits, therefore it is important to educate the general population, and especially young people, to the importance of a healthy and balanced diet. To this end, understanding the real eating habits of people becomes fundamental for a better and more effective intervention to improve the students' diet. In this paper we present two exploratory analyses based on centroid-based clustering that have the goal of understanding the food habits of university students. The first clustering analysis simply exploits the information about the students' food consumption of specific food categories, while the second exploratory analysis includes the temporal dimension in order to capture the information about when the students consume specific foods. The second approach enables the study of the impact of the time of consumption on the choice of the food.Source: PAP 2018 - The 2nd International Workshop on Personal Analytics and Privacy, pp. 156–171, Dublin, Ireland, 10-14 September 2018
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Database of female scientists at the 6 targeted Departments of UNIPI
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality.
The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends.
The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration.
This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality.
In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its
evolution, and scientific production.
The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different
departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career.
This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
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