Nuove metodologie di apprendimento digitale: Microlearning e mobile learning

La tesi tratta in modo approfondito le tipologie di apprendimento non tradizionale, ovvero in contesto non scolastico/universitario, focalizzandosi sull'importanza che hanno i dispositivi mobili come mezzo di aggiornamento e miglioramento costante delle conoscenze e abilità delle persone. Queste nuove metodologie sono chiamate microlearning e mobile learning, evoluzioni naturali dell'e-learning nate dall'esigenza di un apprendimento che non fosse più solo a distanza, ma applicabile al contesto mobile per rispecchiare le nuove esigenze delle persone.

Sviluppo di un’app mobile con Realtà Aumentata e Machine Learning in contesto di biodiversità

Il volume di tesi ha riguardato lo sviluppo di un'applicazione mobile che sfrutta la Realtà Aumentata e il Machine Learning nel contesto della biodiversità. Nello specifico si è realizzato un modello di AI che permetta la classificazione di immagini di fiori. Tale modello è stato poi integrato in Android, al fine della realizzazione di un'app che riesca a riconoscere specifiche specie di fiori, oltre a individuare gli insetti impollinatori attratti da essi e rappresentarli in Realtà Aumentata.

My Personal Trainer: applicazione di e-learning per il fitness su piattaforma Android

L’obiettivo della tesi è quello di mettere in pratica e approfondire le conoscenze acquisite durante il percorso universitario, al fine di avvicinarsi a quello che sarà poi il mondo del lavoro. Questa motivazione e la voglia di realizzare qualcosa di concreto hanno portato alla scelta di sviluppare un’applicazione per sistemi mobile, in questo modo è stato necessario affrontare le varie fasi di sviluppo di un software che comprendono in particolare la progettazione e l’implementazione. All'interno della tesi si darà uno sguardo al contesto in cui l’applicazione MyPersonalTrainer vuole andarsi ad inserire, si procederà con la descrizione della fase di progettazione che comprende l'analisi dei requisiti, poi si analizzerà la fase di implementazione e infine verranno effettuate delle considerazioni sui possibili sviluppi futuri.

Design and implementation of a Reinforcement Learning framework for iOS devices

Reinforcement Learning is an increasingly popular area of Artificial Intelligence. The applications of this learning paradigm are many, but its application in mobile computing is in its infancy. This study aims to provide an overview of current Reinforcement Learning applications on mobile devices, as well as to introduce a new framework for iOS devices: Swift-RL Lib. This new Swift package allows developers to easily support and integrate two of the most common RL algorithms, Q-Learning and Deep Q-Network, in a fully customizable environment. All processes are performed on the device, without any need for remote computation. The framework was tested in different settings and evaluated through several use cases. Through an in-depth performance analysis, we show that the platform provides effective and efficient support for Reinforcement Learning for mobile applications.

Fighting child marriage in developing countries: design and development of a mobile application

Child marriage is still a great issue in developing countries and even if the interventions to prevent it are having results, they are not enough to eliminate the problem. Among the strategies that seem to work most to fight child marriage, there is the empowerment of girls with information combined with education of parents and community. As smartphones are more accessible year after year in developing countries, this thesis wants to investigate if a mobile app could be effective in fighting child marriage and which characteristics such an app should have. The research was organized in four phases and used design and creation and case study methodologies. Firstly, the literature was analyzed and an initial design was proposed. Secondly, expert interviews were performed to gain feedback on the proposed design, and afterwards prototype was built. Thirdly, a case study in the Democratic Republic of Congo (DRC) was performed to test the prototype, gaining insights and improvements through group interviews with 26 girls aged 15-19. Finally, a first version of the app was developed and a second phase of the case study was run in the DRC to understand if the girls were able to use the app. This phase included 14 girls of which 6 had participated in the prototype testing and used questionnaires as a data generation method. The app was built following the Principles for Digital Development. Even if this app is built based on the case study in DRC is modular and easily adaptable to other contexts as it is not content-specific. It was shown that is worth continuing to study this topic and it was defined a conceptual framework for designing learning apps for developing countries, in particular, to fight child, early, and forced marriage.

Trajectory Planning in UAV-Aided Wireless Networks Via Reinforcement Learning

This thesis is focused on the design of a flexible, dynamic and innovative telecommunication's system for future 6G applications on vehicular communications. The system is based on the development of drones acting as mobile base stations in an urban scenario to cope with the increasing traffic demand and avoid network's congestion conditions. In particular, the exploitation of Reinforcement Learning algorithms is used to let the drone learn autonomously how to behave in a scenario full of obstacles with the goal of tracking and serve the maximum number of moving vehicles, by at the same time, minimizing the energy consumed to perform its tasks. This project is an extraordinary opportunity to open the doors to a new way of applying and develop telecommunications in an urban scenario by mixing it to the rising world of the Artificial Intelligence.