ML-based intelligent real-time feedback system for blended classroom

The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality. However, there are abundant scopes of improvement and strong need for further careful investigations in this area. We propose an ML-based intelligent real-time feedback system to address current research challenges for blended classrooms. The proposed system provides real-time feedback to students and teachers. We build an Android application for our intelligent feedback interfaces. The user interfaces use students' academic performance prediction models with real-time states and dynamic feedback timings based on historic feedback statistics. In addition, the feedback scheduling algorithms, choices of peripheral devices for real-time feedback, and feedback modalities to optimize fatigue make our system interfaces intelligent and novel. The end users well-received the intelligent features and technology of the proposed system. Our empirical findings indicate that unique design elements, such as dynamic timing, choice of peripheral devices, and modalities of real-time feedback, are crucial in integrating the system with blended classes. The intelligent characteristics of the proposed system have been appreciated by a large proportion of the end-users (90.90% of teachers and 84.21% of students) for use in real-time blended classroom environments. The higher comparative system usability scale (SUS) scores with benchmarks show real promise of the system design.