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Introduce Computational Thinking with the Music Industry


How can you stimulate students and teachers to engage in computational thinking with data? One key is to provide a realistic, relevant, and meaningful context. We’re excited to release two new resources—a short video and a two-page handout—that teachers can use to introduce students to how the music industry is using data and computation.

A typical context for computational thinking with data is sports, but not every student is motivated to play moneyball with baseball stats. Another can be data from school events, like ticket sales from the school’s theater production. School-based data are relevant, but often not particularly interesting to work with. In the context of our National Science Foundation-funded Playful Assessment project, we’ve found the music industry provides a wonderfully realistic, relevant, and meaningful context for middle school students.

How teachers can use the music industry to engage students with data

In our video “A Visit to Chartmetric“, leaders at a real start-up company explain why musical artists and their producers use data dashboards—for example, to plan a tour that promotes the artist in regions where they have followers. The Chartmetrics team also explains what computational thinking with data looks like in the music industry, and why computers are needed to collect, store, analyze, and make inferences from data. The data comes from streaming services, Wikipedia, social media and other places where fans engage with artists.

In a related two-page handout, a leader at Pandora describes how the streaming service uses computational thinking to give artists data that help them grow their fan base. The handout highlights the importance of using computers to visualize data in ways that artists find insightful. It also describes how today’s musical artists use data to decide who to tour with, how they use maps, and what data can tell an artist about how their career is going.

Both the video and the handout can be used by teachers to start a conversation with students about what they know about computational thinking with data. What do the students know about the data that is collected when they stream a song or follow an artist? Can they imagine how artists would reason about data to build their careers? Why are computers used to store, organize, and access data about how people interact with music? What kind of computational processes go into making a useful chart about trends in an artist’s popularity? And what are the privacy and other implications of data becoming such a big part of the music industry today?

The video and handout were produced as part of a larger research project to develop a game that engages students in computational thinking and provides useful formative assessment information to teachers about computational thinking. The game, BeatsEmpire, is not yet released. It positions students as managers of musical artists. In that role, students can use insights from computational thinking with data to grow the fame and fortune of their artists. To get more information about the game, leave a comment with your email address below.

Want to know more about Computational Thinking? Find more resources here:

Based on work supported by the National Science Foundation under grants #1742011 and #1741956. Opinions expressed are those of the authors and do not necessarily reflect the views of NSF. Digital Promise, SRI International, Teachers College, Georgia Tech, and the University of Wisconsin, Madison, are BeatsEmpire partners.

Authors

Jeremy Roschelle

Co-Executive Director, Learning Sciences Research

Read bio

Jeremy Roschelle, Ph.D., serves as co-executive director of Digital Promise’s Center for Learning Sciences Research (CLSR) where he leads research on the future of learning with technology. He co-leads the NSF Engage AI Institute and leads the U-GAIN Reading R&D Center, both of which investigate advanced uses of AI in education. Dr. Roschelle is a Fellow of the International Society of the Learning Sciences, and is internationally known for his work on artificial intelligence, computer-supported collaborative learning, learning mathematics with technology, and on scaling up research-based technologies. Over his 30+ year career, he has authored about 200 publications. His publications have about 28,000 citations and he holds 10 patents.

At Digital Promise, Dr. Roschelle oversees a dedicated group of researchers and learning scientists working to improve teaching and learning across preschool, PreK-12, and postsecondary programs. His focus is particularly on improving the infrastructure for generating research evidence and creating an infrastructure for learning-sciences-based applications of AI. Programs including SEERNet, AIMS EduData, and SafeInsights are creating state-of-the-art approaches to generating evidence on digital learning platforms. The K-12 AI Infrastructure Program is funding public goods, cultivating datasets, benchmarks and models that are grounded in the learning sciences.

Prior to joining Digital Promise, Dr. Roschelle led some of the most important research studies on the efficacy of technologies for learning mathematics. He is also well-known for his contributions to collaborative learning and his leadership of several national research hubs related to educational technology. Over the course of his career, he has secured and managed tens of millions of dollars in highly competitive external funding from federal agencies and global foundations. His research has contributed to policy, products, and into practical applications in schools. He also communicates about learning sciences research and AI directly to the public through his podcast, Future Fluent, and through his large following on LinkedIn.

Dr. Roschelle earned an bachelor’s degree in Computer Science  from the Massachusetts Institute of Technology and a Ph.D. in learning sciences from the University of California, Berkeley. He enjoys jazz, backpacking and photography.