Immersive Analytics Decision Support System for the Education Domain - CIDER

Overview

This project seeks to evaluate a Virtual Reality (VR) based data visualization system called the System Exploration and Engagement environment (SEEe) and understand how learning and interaction happens in a multi-modal data environment within the education context. The SEEe environment aims to foster constructivist learning kind of active learning in VR by providing qualitative content such as videos covering education experiences and perspectives on factors impacting literacy in their community created by community members, text in the form of relevant new articles, and quantitative data such as graphs, bar chart data populated on the geographic map of Maryland containing school data.

Research Questions

R1: How do immersive multi-format data visualization systems support inquiry-based learning of complex systems?

R2: What sensemaking strategies do users adopt in immersive analytics environments?

Problem Statement

While immersive analytics systems like SEEe hold promise for supporting complex, inquiry-based learning, little is known about how users—especially future educators—interact with multi-modal data in VR environments. The lack of usability insights and cognitive load evaluation creates a barrier to effectively integrating such tools into educational contexts.

User & Audiences

• Primary Users: Teacher candidates from UMBC’s Sherman Center for Early Learning and the Education Department, as well as in-service educators from external institutions--Towson University, and in-service career teachers. These users directly interacted with the SEEe system during the study.
• Target Audience: Educators, researchers in immersive learning and educational technology, HCI/UX professionals working with VR environments, and developers designing immersive analytics systems for education and decision support.

Roles & Responsibilities

• My Role: Graduate Student Researcher
• Team Member: Priya Rajasagi (PI), Dr. Anita Komlodi, Imaging Research Center at UMBC
• Responsibilities:
  • Analyzed recorded video where users engaged with and made sense of layered data (maps, videos, charts)in immersive environments and identified the usability issue on SEEe.
  • Analyzing user feedback using the User Experience Questionnaire (UEQ) and NASA Task Load Index (TLX)
  • Synthesizing post-questionnaire data to assess usability and cognitive workload
  • Contributed to the ASIS&T 2025 conference paper based on the study

Scope & Constraints

• Timeline: November 1, 2023 – May 20, 2025
• Participants: 20-24 teacher candidates from UMBC's Sherman Center, Education Department, as well as from external institutions, such as Towson University and in-service career teachers
• Constraints: VR lab location, equipment availability, compensation, and motion sickness risk.

Process & What I Did

The study consisted of three sessions:

• Session 1 (virtual): Consent, demographic collection, and 2D conceptual modeling (FigJam).
• Session 2 (in-lab): VR training, practice task, and main study, where participants employed the think-aloud approach, and post-task interview.
• Session 3 (in-lab): Collaborative conceptual modeling and post-study surveys.

Data collected included audio/video recordings, interview transcriptions, participant-created conceptual models, and survey responses measuring mental workload and user satisfaction.

Outcomes & Results

• High engagement: Participants rated Novelty and Stimulation highly (+2.24), appreciating SEEe’s immersive format.
• Key pain points: Participants struggled with menu navigation and connecting nodes to data elements.
• Cognitive load: NASA-TLX scores showed high mental demand but low frustration and physical strain.
• Design suggestions: Simplify menus, improve labeling, enable direct manipulation of visualizations.

This work is under review at the ASIS&T 2025 conference and highlights key usability dimensions for immersive analytics tools in education.