4 citations · 13 across the 6 of their papers we have counts for
6 papers
Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Claudio Bonesana, Daniele Malpetti, Sandra Mitrović +2
We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in con…
Intelligent tutoring systems by Bayesian nets with noisy gates
Alessandro Antonucci, Francesca Mangili, Claudio Bonesana +1
Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When…
Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment
Giorgia Adorni, Francesca Mangili, Alberto Piatti +2
In modern and personalised education, there is a growing interest in developing learners' competencies and accurately assessing them. In a previous work, we proposed a procedure fo…
Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach
Francesca Mangili, Giorgia Adorni, Alberto Piatti +2
Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems. An assessment rubric typically and effectively describes relevant competencies a…
ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks
Claudio Bonesana, Francesca Mangili, Alessandro Antonucci
We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. Adaptiveness is intended here as the dynamical ch…
A New Score for Adaptive Tests in Bayesian and Credal Networks
Alessandro Antonucci, Francesca Mangili, Claudio Bonesana +1
A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, ar…