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Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Computers in Education 4 - Online and Distributed Learning I

Page Count

15

Permanent URL

https://peer.asee.org/41168

Download Count

60

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Paper Authors

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Mia Taylor Virginia Polytechnic Institute and State University

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Mia Taylor is a Master's Student (MS) at Virginia Tech. She holds a Bachelor of Science (BS) degree in Computer Science from the same institute. She has industry experience in applied natural language processing, software engineering, and full-stack web development. Her research interests include applied natural language processing, interactive machine learning, and information visualization. She hopes to continue working in these research areas in industry after her 2022 graduation.

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Danny Mathieson Virginia Polytechnic Institute and State University

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Danny Mathieson holds a MAEd in Integrative STEM Education and BS in Sociology from Virginia Tech. He is currently pursuing an MEng in Computer Science after serving several years as a Technology Education Instructor. Prior professional roles have also included Director of Education for Pathways Inc., adjunct faculty at John Tyler Community College, and a Teach for America member.

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Leanna House Virginia Polytechnic Institute and State University

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Chris North

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Dr. Chris North is a Professor of Computer Science at Virginia Tech in Blacksburg, VA, USA. He is Associate Director of the Sanghani Center for AI and Data Analytics, and leads the Visual Analytics research group. His research and education agenda seeks to enable effective human-AI interaction for big data analysis.

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Abstract

Collaborative data analysis enables students to explore and analyze multi-dimensional data together. This case study shows how collaborative data analysis can be successfully integrated into teaching the engineering design process using novel software called Andromeda. Andromeda is an interactive tool that seeks to make analysis of multi-dimensional data accessible for novices. Multi-dimensional data is often difficult for people to understand because it relates multiple variables, for example all the many factors that might make a racecar design faster or slower. Andromeda visualizes the data in a two-dimensional plot, such that data points (e.g. racecar designs) that are considered similar in the multi-dimensional sense are plotted closer to each other. Analysts can interactively explore relationships and trade-offs in the data by dragging data points and sliders. Now, we show that Andromeda has collaborative utility in a classroom setting and as a public, educational resource for teachers and students. This case study performs an observational, qualitative analysis on the collaborative use of Andromeda in an 8th grade technology education class. Students were given two engineering projects through \whitebox{}: Survival Shelter 2.0 and Dragster 2.0. \whitebox{} is a web-based STEM education software that allows students to learn STEM concepts, such as introductory physics, and practice the engineering design process. Survival Shelter 2.0 and Dragster 2.0 are two design projects that let students create an emergency survival shelter for hikers and a $CO_{2}$ racecar, respectively. In this case, students used \whitebox{} to create, analyze, and simulate their project designs. Between design iterations, the class explored their designs in Andromeda with the teacher acting as the facilitator. That is, the teacher uploaded data describing the students' projects to Andromeda; each point in the visualization represented a student's design. With the teacher controlling Andromeda, students used Andromeda to visualize, analyze, and compare their designs. Data were collected from students, along with impressions from their teacher. We use these data to assess the success of the class collaboration. According to both the teacher and the students, Andromeda offered an opportunity for both collaborative and personal reflections. For example, students engaged in extended conversation with each other and the teacher while, collectively, the class explored their design-related data. Also, students felt that Andromeda helped them to compare their designs to other students in a friendly, but competitive manner. Finally, survey responses from the students support the known advantages of Andromeda by showing that, despite not having the mathematical background to understand dimensionality reduction, students learned about relations between variables and enjoyed using Andromeda. Despite seeing clear advantages from using Andromeda in a collaborative, class setting, we discovered that Andromeda was slightly misused. Web-based Andromeda in its current form requires all data variables to be continuous. However, in this case study, the definition of continuous was conflated with numerical. We discuss the implications of these inaccuracies and potential improvements to Andromeda to improve its utility as a public, educational resource. We also provide an example class activity aligned with Virginia's proposed Standards of Learning in data science. Hopefully, this will inspire educators to introduce collaborative data analysis tools such as Andromeda into their classrooms.

Taylor, M., & Mathieson, D., & House, L., & North, C. (2022, August), Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41168

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