The convergence of the Internet of Things (IoT) devices, machine learning (ML) algorithms, and cloud computing infrastructure is radically changing the education paradigms as they can create new personalized learning experiences on a level never before seen. In this article, the entire technological system behind the scenes of smart classroom analytics, sensor networks, real-time data processing architectures, and adaptive learning systems, which dynamically changes content delivery depending upon individual student performance measures. Due to the examination of large-scale applications in several school districts, the improvement of academic results, especially among at-risk students, is pronounced, and the article reveals the advanced nature of the processes that occur behind the scenes of personalized content delivery and early intervention schemes. The article raises important questions of privacy and ethics and discusses regulatory frameworks of compliance, including FERPA, and best practices that would allow protecting student data conducive to meaningful analytics. The article ends with a discussion of some of the new technologies, such as quantum computing, extended reality, and brain-computer interfaces, but also considers the old issue of the digital divide and provides policy suggestions about sustainable and equal implementation of educational analytics systems that augment and do not supplant the underlying pedagogical relationships.

Educational analytics Personalized learning IoT in education Machine learning algorithms Student data privacy