Human-Object Interaction (HOI) recognition is crucial for understanding the dynamics within educational environments, particularly in student sessions. Current HOI methods predominantly utilize transformer-based architectures due to their capability of capturing contextual interactions effectively. However, existing approaches face significant challenges, including insufficient fine-grained contextual information and limited applicability in specialized domains such as educational settings. This study introduces an innovative deep learning framework integrating HOI detection with advanced Machine Learning Operations (MLOps) and DataOps practices tailored explicitly for analyzing student interactions within educational sessions. We address the critical gap in available datasets by creating a new dataset capturing diverse interaction scenarios typically observed in educational sessions. Our dataset design includes detailed criteria for data selection and annotation, ensuring robust and high-quality interaction representations. Empirical evaluations demonstrate that our proposed integrated HOI-MLOps framework not only achieves superior interaction recognition accuracy compared to traditional models but also significantly enhances scalability and efficiency through streamlined data operations. This study’s contributions provide practical insights into leveraging HOI recognition in educational analytics, ultimately aiding in more effective monitoring and evaluation of student activities in educational environments.