Towards a ubiquitous students' response system for monitoring learning performances

Receiving feedback from students about their learning experience is a key part of any pedagogical approach. Students' feedback could be retrieved in a variety of ways using various students response systems (SRS); however, existing SRS suffer from lack of seamless integration into learning environments, becoming a potential source of distraction for the learning process. We propose a ubiquitous students response system (U-SRS) that is capable of continuously and seamlessly monitoring various students' learning performances features, making sense of them and providing insights for teachers, enabling them to adapt their pedagogical approach according to their students immediate needs. The proposed U-SRS takes advantages of machine learning and the internet of things paradigm to enable its services in connected classrooms. We present our solution's design, its architecture and features used to build learning performance predictive models along with implementation and various prototypes. Finally, we highlight the advantages of U-SRS over existing solutions.