The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational settings has revolutionized the traditional teaching-learning environment, giving rise to the concept of smart classrooms. This transformation not only enhances the monitoring of people and processes but also impacts the dynamics of student behaviors, which are influenced by various factors such as the teaching environment and teacher-student interactions. Recognizing and accurately identifying these behaviors is crucial for educators to implement effective interventions and improve learning outcomes. This study conducts a systematic literature mapping to examine the contemporary landscape of student behavior identification in smart classrooms. The analysis reveals a rich diversity of methodologies and approaches used in this area. Key contributions of this research include the development of taxonomies for 37 technologies deployed, 25 challenges faced, 58 student behaviors observed, as well as the identification of nine subjects benefiting from this data and 10 methodologies for processing behavioral information. By offering an overview of the technological and methodological underpinnings of behavior identification in smart classrooms, this study significantly propels forward the domain of smart classroom research, equipping educators and technologists with a holistic understanding of how to navigate and leverage the complexities associated with monitoring student behavior.