Personally Meaningful Data to Motivate Learning in Data Science and AI

I have written several blog posts about the different ways to implement Media Computation in introductory programming courses. We built JES at Georgia Tech in 2002 and the final release was in 2020. Our introductory course in PCAS that uses Media Computation, COMPFOR 121: Computing for Creative Expression, uses Snap! and Pixel Equations (as described in this blog post). Our Python course (COMPFOR 221: Digital Media with Python) started in Python3 with the JES4Py library, but then we moved to Google Collaboratory notebooks (the libraries for that are available here).

Dave Largent at Ball State continues to teach Media Computation. The students in his course compete in an art show each term for which I’ve served as a judge. Dave let me know that he and his students have extended JES4Py and have released a new library:

I’ve had a couple of undergrad students working with me to redevelop/extend Gordon College’s JES4py package. We’ve published it at PyPI under the name mediaComp (https://pypi.org/project/mediaComp/). Our GitHub is https://github.com/dllargent/mediaComp.

Why is this interesting? Why is anybody teaching with a 20 year old method, and even making new libraries for it?

Maybe because it answers a CS education need that has only grown more important. Data science is a bigger deal now than it was 20 years ago. Ben Shapiro told us in 2018 that machine learning was going to change the CS curriculum, and we needed to think more about data. But what data are interesting to students?

The empirical studies in computing education research are pretty clear: Motivation matters.. Students get frightened off by computer science classes. They find our examples boring. If we can teach the same computing concepts using any data, why not use data that students find interesting?

I’ve heard Jens Mönig, lead architect and developer on Snap!, answer this question several times in several talks. There’s a new interview with him in the most recent ACM Inroads magazine (link) with Jens where he makes the point again. Students are interested in their data. Personal data, data about them, data that they make, data that are relevant to them. The phrase in the Constructionist community is “personally meaningful.”

Media Computation is data manipulation with personally meaningful data — your pictures and sounds, or the pictures and sounds that interest you. There are a lot of pixels and samples in those pictures and sounds. Those are data that matter to the students who care about those pictures and sounds.

Media Computation as an approach is not going to be for everyone. But every computing teacher should answer the meta question, “Why should my students care about these data?”. We often use the Corgis project data to help students find data that are personally meaningful. That’s where you can find the Titanic passenger dataset that Jens talks about in his interview. I am an advisor to API Can Code, which is a curriculum all about doing data science with live data that students might care about.

My point here isn’t that all teachers should use Media Computation. My point is that all computing teachers should engage students with personally meaningful data.

Student collage from COMPFOR 221 last year