Facilitating feedback at university using AI-based techniques

Many recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, guides institutions in making informed decisions, ensures continuous improvement, fosters engagement and motivation, facilitates open communication, and enables personalized learning experiences. However, despite its relevance, the use of feedback processes in everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses. On the other hand, the expansion of ubiquitous learning in digital environments has led to an exponential growth of significant data for tracking learning. Although the use of these data can be beneficial, tools and technologies are needed for automated data collection and analysis. In this direction, significant support can be provided by technologies incorporating Artificial Intelligence (AI), which include a wide collection of different technologies and algorithms. Notably, Learning Analytics (LA) and Educational Data Mining (EDM) can be useful in developing a student-focused strategy. The systematic use of AI techniques and algorithms could enable new scenarios for educators, profiling and predicting learning outcomes and supporting the creation of sustainable patterns of assessment. However, even though several studies aimed at integrating EDM and LA techniques in online learning environments, only few of them focused on applying them to real-world physical learning environments to support teachers in providing timely and quality feedback based on minimally invasive measurements. The present paper presents an approach aimed at addressing the feedback problem in real university classes, laying the groundwork for the development of an intelligent system that can inform and support the university teacher in delivering personalized feedback to a large group of students.