| Sumario: | Background: Traditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of insights. Objectives: This study aims to explore the potential of multimodal learning analytics as a formative assessment tool in math education. The focus is on discerning how collaborative discourse behaviours and emotional indicators interplay with lesson evaluation performance. Methods: Using undergraduate students' multimodal data, which includes collaboration data, facial behaviour data, and emotional data, the study explored the patterns of collaboration and emotion. Through the lens of multimodal learning analytics, we conducted exploratory data analysis to identify meaningful relationships between specific types of collaborative discourse, facial expressions, and performance indicators. Moreover, the study evaluated a machine learning model's potential to predict target learning outcomes by integrating data from multiple channels. Results: The analysis revealed key features from both discourse and emotion data as significant predictors. These findings underscore the potential of a multimodal analytical approach in understanding students' learning process and predicting outcomes. Conclusions: The study emphasizes the importance and feasibility of a multimodal learning analytic approach in the context of math education. It highlights the academic and practical implications of such an approach, along with its limitations, pointing towards future research directions in this area. Lay Description: What is currently known about this topic?: Learning analytics has emerged as a powerful tool that aligns with the purpose of formative assessment, enabling educators to monitor and understand students' learning.The primary focus of traditional learning analytics research has been on online learning environments, relying mostly on unimodal data. This perspective offers a limited view, as learning is inherently multimodal.Formative assessment stands as a cornerstone of effective learning, with a rich body of evidence confirming its positive role in improving teaching and learning processes. What does this paper add?: The study implemented game‐based learning lesson critique activity with students and explored the use of Minecraft education version as a tool for interactive math lessons.The study showcased the complex patterns of collaboration and interactions during game‐based learning activities, highlighting the integral role of teamwork.By analysing verbal and non‐verbal cues, the study illuminated various features of collaborative dynamics when evaluating game‐based lesson activities with peers. Implications for practice/or policy: The study's findings can inform the design and implementation of multimodal learning analytics as a formative assessment tool. This can promote effective formative assessment and adaptive support mechanisms for students in mathematics education within a digital game‐based learning environment.By identifying areas of improvement and specific needs, practitioners can tailor interventions to address challenges faced by learners in their collaborative efforts and digital content evaluation.The study contributes to the growing literature on multimodal data analytics and its applications in education, projecting its role in various educational contexts and fostering innovative methods for data analysis and interpretation.
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