02976nas a2200373   4500008004100000020002200041245008700063210006900150260005200219520183300271100002102104700001902125700001802144700002002162700002102182700002102203700001902224700002402243700001802267700002302285700001902308700001902327700002102346700001802367700001802385700001902403700002002422700001602442700002002458700001902478700002002497700001802517856006702535       2020                        eng d  a978-3-030-54994-700aAnalysis and Visualization of Performance Indicators in University Admission Tests0 aAnalysis and Visualization of Performance Indicators in Universi  aChambSpringer International Publishingc2020//3 aThis 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 question 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 composition 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.1 aNatilli, Michela1 aFadda, Daniele1 aRinzivillo, S1 aPedreschi, Dino1 aLicari, Federica1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1401426nas a2200133   4500008004100000245009200041210006900133260001900202520089400221100002001115700002001135700001901155856011801174       2014                        eng d00aAn abstract state machine (ASM) representation of learning process in FLOSS communities0 aabstract state machine ASM representation of learning process in  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/abstract-state-machine-asm-representation-learning-process-floss-communities