A Ubiquitous Students Responses System for Connected Classrooms
Receiving feedbacks from students about their learning experience is a key part of any pedagogical approach. Students’ feedbacks could be retrieved in a variety of ways using various Students Response Systems (SRS). A major drawback of existing SRS is their lack of seamless integration into learning environments. As such, they become a potential source of distraction for the learning process. We believe technology should blind seamlessly and provide support for pedagogy, thus, we propose a Ubiquitous Students Responses 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 and describe its architecture. We select a subset of relevant features, collected by connected objects, and used by machine learning algorithms to build learning performance predictive models. Finally, we highlight the advantages of U-SRS over exiting SRS solutions.
KeywordsStudents Responses System Students feedback Internet of Things Machine learning
The work of A. Karkouch leading to these results has received funding from the Moroccan National Center for Scientific and Technical Research under the grant N° 18UCA2015.
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