OpenMMLA: an IoT-based Multimodal Data Collection Toolkit for Learning Analytics

Multimodal Learning Analytics (MMLA) expands traditional learning analytics into the digital and physical learning environment, using diverse sensors and systems to collect information about education in more real-world environments. Challenges remain in making these technologies practical for capturing data in authentic learning situations. With the advent of readily accessible powerful artificial intelligence that includes multimodal large language models, new opportunities are available. However, few approaches allow access to these technologies, and most systems are developed for specific environments. Recent work has begun to make toolkits with access to collecting data from sensors, processing, and analyzing, yet these tools are challenging to integrate into a system. This paper introduces OpenMMLA, a toolkit approach that provides programming interfaces for harnessing these technologies into an MMLA platform with prebuilt pipelines, including the audio analyzer, indoor positioning, and video frame analyzer, offering multimodal data collection and visualizations and analytics. The paper provides an initial evaluation of the functionalities of the toolkit in data capturing and the implemented pipelines' performances.