The field of multimodal, analytics learning analytics – combining multiple data streams to provide richer insights into learning processes – continues to grow and evolve (Ochoa et al., 2022, Worsley et al., 2016; Yuan et al., 2025). This growth can be attributed to multiple factors including novel computational modeling techniques, technologies for collecting rich streams of learning and interaction data, and the greater emphasis in education on assessing higher order thinking skills. Recently, research has moved beyond modeling individual users to multiparty settings including small groups of students and even entire classrooms (e.g., Subbaraj et al., 2020). In addition, the growth of multimodal foundational large language models (e.g., Küchemann et al., 2025) has greatly increased the ability to process and fuse multiple modalities of data with reduced needs for training data.  While there is great promise for the field, there are significant challenges with detecting and reducing bias in algorithms, maintaining privacy of data, validating performance of models of complex educational interactions, and scaling and implementation of these approaches in the wild. 

The objectives of the workshop are to bring together a diverse group of researchers who are researching and developing multimodal analytics for education and to share their methodology and illustrate techniques on their own data. It is expected that the workshop will help the EDM community by improving researchers’ approaches for incorporating multimodal, multiparty learning analytics (MMLA) to provide a more comprehensive understanding of learners working individually, in small groups, and in whole-class settings. The workshop will address key considerations including data collection of different modalities, techniques for integrating multiple data sources from multiple individuals, advances in AI-based analysis techniques, multimodal analytic pipelines, and approaches to addressing data privacy and ethical frameworks.