  Investigating children's embodied learning in mixed-reality environments,
where they collaboratively simulate scientific processes, requires analyzing
complex multimodal data to interpret their learning and coordination behaviors.
Learning scientists have developed Interaction Analysis (IA) methodologies for
analyzing such data, but this requires researchers to watch hours of videos to
extract and interpret students' learning patterns. Our study aims to simplify
researchers' tasks, using Machine Learning and Multimodal Learning Analytics to
support the IA processes. Our study combines machine learning algorithms and
multimodal analyses to support and streamline researcher efforts in developing
a comprehensive understanding of students' scientific engagement through their
movements, gaze, and affective responses in a simulated scenario. To facilitate
an effective researcher-AI partnership, we present an initial case study to
determine the feasibility of visually representing students' states, actions,
gaze, affect, and movement on a timeline. Our case study focuses on a specific
science scenario where students learn about photosynthesis. The timeline allows
us to investigate the alignment of critical learning moments identified by
multimodal and interaction analysis, and uncover insights into students'
temporal learning progressions.
