A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments
This work simplifies tasks for learning scientists analyzing children's collaborative learning in mixed-reality environments, but it is incremental as it presents an initial case study for feasibility.
The study tackled the challenge of analyzing complex multimodal data in embodied learning environments by using machine learning and multimodal learning analytics to support Interaction Analysis, resulting in a visual timeline representation of students' states, actions, gaze, affect, and movement to investigate alignment with critical learning moments.
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.