This project addresses the challenge of monitoring large, dynamic classrooms by proposing a privacyoriented multimodal data acquisition system tailored for MMLA. Traditional learning analytics rely on unimodal data and fail to capture complex classroom interactions. In contrast, MMLA leverages multiple data sources to better understand learning behaviors. Current systems lack adaptability, userfriendliness, and privacy considerations, impeding their integration into classrooms. The proposed system comprises static and dynamic nodes, with dynamic nodes worn by individuals and static nodes strategically placed in classrooms. Data features, selected on MMLA relevance, are transmitted wirelessly to the static node for storage and analysis. Privacy is prioritized by avoiding sensitive data collection and adhering to GDPR guidelines. The design ensures adaptability, supporting additional sensors and seamless integration into various educational settings. This foundational system enables future research while addressing ethical and technical challenges in large-scale classrooms.