PhD Dissertation Proposal: Allison Poh, A Multimodal Dashboard for Real-Time Teacher Support During Embodied Learning Activities

Content

Speaker:

Allison Poh

Abstract:

Embodied learning is an educational approach that holistically connects body and mind, grounding learning in movement, perception, and interaction with the world. These activities can range from small hand gestures (e.g., making geometric shapes with hands) to full-body movements (e.g., walking along an oversized number line). While recent research highlights embodied learning’s benefits for students’ cognitive, affective, and psychomotor outcomes, adoption in K–12 classrooms remains low, in part because teachers lack adequate support. To provide teachers with real-time support, the field of learning analytics has developed tools such as teacher dashboards, which collect, analyze, and visualize student data during learning activities. Recent work has begun to explore how multimodal teacher dashboards (i.e., those that draw on more than one modality, such as video, audio, and sensor data) can support teachers during embodied learning. However, existing dashboards are designed for a single activity and support only one type of embodiment.

This thesis explores the design of a multimodal teacher dashboard that is not tied to a single activity or embodiment type, but instead can flexibly support a range of embodied learning activities. To pursue this goal, I will design a multimodal teacher dashboard for WearableLearning, a novel platform that allows anyone to create and play embodied educational games. The ability to create any embodied game makes WearableLearning versatile and an ideal testbed for developing a dashboard capable of supporting a range of embodied learning activities.

This thesis builds on my prior work of designing a prototype teacher dashboard for WearableLearning through a teacher-centered approach. While a prototype exists, it focuses on conventional dashboard support (e.g., tracking student progress) rather than capturing students’ embodied motions and connecting them to learning. To address this gap, I will continue a teacher-centered approach guided by three questions: (1) How can multimodal data be leveraged to provide real-time support to teachers across a range of embodied learning activities? (2) In what ways do teachers use a multimodal dashboard to assess, support, and monitor students during these activities? and (3) How can a multimodal dashboard support teacher reflection on the design and effectiveness of embodied learning activities?

To answer these questions, I propose a three-phase plan. First, I will conduct an exploratory study with pre-service teachers to understand their perceptions and preferences for support during embodied learning activities, including their views on different types of data modalities. Second, I will extend the existing prototype with new dashboard features based on insights from the exploratory study. Finally, I will conduct an in-the-wild study in middle school classrooms to examine how teachers use the dashboard in practice and how it supports them both during and after activities. This work will contribute new understanding of how multimodal data can capture embodiment, design principles for dashboards that flexibly support diverse embodied learning activities, and empirical evidence of how real-time analytics can enhance teaching and reflection in embodied learning contexts.

Advisor:

Ivon Arroyo