SEEe

Overview

SEEe is an immersive analytics system developed as part of the Whole Challenge initiative by UMBC's Imaging Research Center. It aims to support inquiry-based learning and sensemaking within complex, multi-data systems, addressing community challenges like education, poverty, and crime in Baltimore neighborhoods.

Research Questions

1. Do immersive analytics systems facilitate inquiry-based learning and sense making within multi-data complex system?

2. What individual sensemaking and learning behaviors do users exhibit in an immersive analytics, multi-data complex environment?

My Role

• UX intern

• Conducted user studies to evaluate learning behaviors and sense making strategies.

• Analyzed participant interactions with SEEe, focusing on how the system supported their understanding and conceptualization of complex data.

Goal

To explore how immersive analytics systems like SEEe can promote critical thinking and enable users to connect diverse datasets to solve real-world problems.

Process

• User Testing: Participants engaged with SEEe to build conceptual diagrams and explore interactive media such as videos, charts, and graphs.

• Data Analysis: Compared pre- and post-experience reflections to measure learning outcomes.

• Interviews: Gathered insights into participants' sense making behaviors, system usability, and learning experiences.

Outcome

• SEEe effectively supported inquiry-based learning by helping participants identify and connect critical factors (e.g., crime, poverty, and institutional racism) impacting education in Baltimore.

• Highlighted the potential of immersive systems to foster deeper understanding and collaborative problem-solving for community-based challenges.