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GraphVisual: Design and Evaluation of a Web-Based Visualization Tool for Teaching and Learning Graph Visualization

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computers in Education Division Technical Session 9: Pedagogical Tools

Tagged Division

Computers in Education

Page Count

16

DOI

10.18260/1-2--34715

Permanent URL

https://peer.asee.org/34715

Download Count

195

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Paper Authors

biography

Martin Imre University of Notre Dame

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Martin Imre is a fourth-yeard PhD candidate at the University of Notre Dame working with Dr. Chaoli Wang on High Performance Computing and Scientific Visualization. His main research focus is summarization and reconstruction of big data using GPU-acceleration and deep learning techniques. He has applied his research in isosurface selection for volume visualization and analysis, graph visualization, and is currently using deep learning techniques for analysis of unsteady flow simulations. He has completed a research internship at Argonne National Laboratory in summer 2018. He received his BSc (2014) and MSc (2016) in Software Engineering at the Vienna University of Technology. During his Master’s program, he conducted research at the VRVis Research Center in Vienna and continued acquiring experience during a research internship at the University of California, Irvine.

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Wenqing Chang Xi'an Jiaotong University

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Wenqing Chang is currently a senior student in Information Engineering from Xi’an Jiaotong University. In 2018, she joined NUS Summer Workshop, developing a 2D webpage game using WebGL and rendering 3D animation using OpenGL. From the fall of 2018 to present, she is a lab researcher in wireless communication, built ambient backscatter enabled secondary communication model and right now is involved in deep learning for joint source-channel coding.

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Shuzhan Wang Beijing University of Posts and Telecommunications

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Shuzhan Wang is an undergraduate student of software engineering at Beijing University of Posts and Telecommunications. Her current research interest is data visualization. She received the Merit Student award for the 2017~2018 academic year at Beijing University of Posts and Telecommunications. In the summer of 2019, she joined the iSURE program at University of Notre Dame supervised by Dr. Chaoli Wang.

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Christine P. Trinter University of Notre Dame

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Christine Trinter is an assistant professor of mathematics education with the Center for STEM Education at the University of Notre Dame where she teaches mathematics content and assessment courses with the Institute for Educational Initiatives' M.Ed. program and data visualization courses for the Notre Dame Education, Schooling, and Society minor. Dr. Trinter's research focuses on factors affecting teacher development, curriculum design, and technology usage in the mathematics classroom and she serves schools both nationally and internationally providing professional development in these areas.

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Chaoli Wang University of Notre Dame

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Dr. Chaoli Wang is an associate professor of computer science and engineering at the University of Notre Dame. He holds a Ph.D. degree in computer and information science from The Ohio State University. Dr. Wang's research interests include scientific visualization, visual analytics, visualization in education, user interface and interaction, and high-performance visualization.

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Abstract

Graphs are ubiquitous in representing data from various fields, such as biological interactions, geographic knowledge, and engineering problems. They allow users to acquire a quick overview of how the data entities are spread and connected in order to gain insights from the data. Many software tools exist to depict and explore graphs. However, there is a lack of tools dedicated to assisting students in learning graph visualization and related concepts. We present GraphVisual, an educational software tool that provides an accessible web interface for students to interact with and explore graph visualizations of real-world data sets of different sizes and characteristics. GraphVisual integrates several popular graph layout algorithms to enable users to discover patterns, outliers, and other features of these data sets. GraphVisual allows users to compare and evaluate different graph visualization techniques from two side-by-side graph display panels, which are supported by a set of interaction functions. To demonstrate the utility of GraphVisual and assess its effectiveness, we conducted a formal user study with two groups: students who were enrolled in a college-level data visualization course and doctoral computer science students who did not take the course. The study includes an introduction, a training session, a survey, and an in-class quiz that is integrated into GraphVisual.

Imre, M., & Chang, W., & Wang, S., & Trinter, C. P., & Wang, C. (2020, June), GraphVisual: Design and Evaluation of a Web-Based Visualization Tool for Teaching and Learning Graph Visualization Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34715

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