A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments

TL;DR

This paper explores using machine learning and multimodal analytics to automate and enhance interaction analysis in embodied learning environments, aiming to reduce manual effort and improve understanding of student engagement.

Contribution

It introduces a novel approach combining machine learning with multimodal data to support interaction analysis in embodied learning, demonstrated through a case study on photosynthesis learning.

Findings

Feasibility of visualizing student states and actions on a timeline.

Alignment of multimodal data with critical learning moments.

Insights into students' learning progressions over time.

Abstract

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.