Computing Applications

Learning Computational Thinking to Dominate the Music Industry

Jeremy Roschelle

One of my early experiences of computing involved using the Music Logo programming language to “program” something that sounded like Beethoven’s Fifth Symphony. Working with MIT professor Jeanne Bamberger, I used music as a context for programming and for inquiry into music. This process re-shaped my views of what computing could be. I came to see computer science as providing a frame of analysis that could reveal the internal structure and patterns within a wide variety of human experiences, not just those that intrinsically involve computers. This experience led to my career as a learning scientist.

More recently, I’ve had the opportunity to use music as a context to get middle school students in New York City excited about data science. I served in a consulting role for a multi-institutional team that developed Beats Empire — a game in which a student manages an artists rise to fame and fortune. To help their artists, the students must demonstrate what they are learning about using data to analyze music industry trends. The game thereby both engages students and can give teachers a sense of what students know and can do. The game is available for free at .


Here I’ll share general thoughts about music as a context for learning about computing.

1. Music as an authentic, accessible context

Students have rich and complex experiences with the music industry today. It’s not only listening to music. Students also talk about how artists use social media. They discuss streaming services and how these recommend music. They think about themes in titles of songs and in the lyrics. They think about who listens to music where and on what devices. This rich encounter of music on computing platforms can set the stage for a learning opportunity where students go behind the scenes to see how computing influences our experience of music.

Indeed, in interviewing music industry experts, we found that experts could easily and cogently explain to students how data is being used to shape artist’s music and careers — and why data science in the music industry can be a great career for women and people of color. For example, see the interview “A Visit to Chartmetric,” where I visited a company that specializes in creating a analytics dashboards for artists and their managers. Chartmetric was kindly willing to explain what they do and why they love their jobs to middle school students.

2. Students’ experience of a drive for data

As a learning researcher, I’ve been involved in many projects that try to involve students with realistic data. Unfortunately, as educators we often come up with “authentic” contexts that aren’t really something that students would ordinarily do. In math, I know I’ve created a “manage a soccer team” unit where students looked at data about how fast team members can run a dash.

One thing I’ve learned is that in a game context, one can simulate a role where data collection is not “assigned” to students, but where they start from a purpose they care about — helping an artist grow their career. In Beats Empire, students sometimes just make spontaneous decisions for their artists. For example, they can recommend a mood or theme for an artist’s song based on their intuition. But they can also collect data in the game, for example on trends in moods and themes. They look into what is popular in particular neighborhoods via an in-game map. The game is set up so that paying attention to data and trends can dramatically increase the success of the player’s artist. This creates a relationship to data that is much more like the real world; data is not as a context for a specific math concept or science principle, but rather as a tool for getting better at what you care about — in this case,  music. This can be exciting to students.

3. Students’ opportunity to iterate with data

It’s also very common in a math or science class to cycle through using data only once. In a science lab, you collect the data, analyze it, report it, and you are done. In this context, the coherence between the processes of collecting, storing, analyzing and interpreting is often only in the eyes of the curriculum designer or teacher — who make sure that the phases of the cycle fit together. But students need to learn this too.

Thus, it is important for students to see how the separate processes of collecting, storing, analyzing and interpreting data constrain each other. If you want to do analyze a trend of a particular kind, then its important to collect and store the data in an appropriate way. A gaming context can create a situation where iteratively looping through processes happens quickly and is essential to the game play. Students can start to connect their choice of analysis types to actions they can take in the game. Likewise, they could decide how to collect data based on the questions they want to answer. There are important lessons to be learned in how to iteratively refine the relationship between data and an overall purpose or initiative.

Students are always learning. Meet them where they are.

Overall, the field of the Learning Sciences recognizes that learning is not a special type of experience that only happens in designated educational settings. People are always learning. Too often, we begin by thinking about what students are NOT learning and then we try to create an artificial experience so they will learn. But its also possible to take a context in which many students are enjoying learning about every day — like music, sports, food or fashion — and think about how to deepen their learning about that experience. One path is by layering computing-rich experiences into the contexts that students are already motivated to learn about. Games can provide a bridge between what students like to learn and enhanced opportunities that will lead towards a career in computing. 

This material is based upon work supported by the National Science Foundation under Grant No. 1742011 & 1741956. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Jeremy Roschelle is Executive Director of Learning Sciences Research at Digital Promise and a Fellow of the International Society of the Learning Sciences.

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