Using Entrepreneurial Mindset and Making to Spark More Accessible AI

What do you think this is a picture of? How would you imagine it connects with a student learning artificial intelligence? A little more on that later but let me share a little on the process of why and how I arrived at this little contraption to teach some AI and machine learning concepts.

KEEN MakerSpark: A Framework for Developing Entrepreneurial Mindset Activities

This week I participated in the KEEN MakerSpark workshop. We looked at how to use “making” and the three C’s of an entrepreneurial mindset (curiosity, connections, and creating value) to improve our engineering curriculum. Since I teach AI and machine learning, I was curious how I could use “making” to visually and tactile”ly” demonstrate how machine learning works to a college student or even a child. In this context, “making” refers to physically making something with your hands. In the context of the 3 C’s, the making is driven by a student’s curiosity, their need to make connections from disparate information, and prototyping a concept or an idea to create value.

Deconstruct/Reconstruct Troublesome Knowledge

In teaching, we often want students to learn a new concept. But there is more to teaching a concept then giving a student a definition, equation, or example. We need to deconstruct all that we know that the student knows and how they arrive at that concept. Troublesome knowledge consists of those engineering or computing concepts that our students seem to struggle with the most. Working backwards, starting at this troublesome knowledge, we then design a learning activity using objectives with observable outcomes and ways to measure their learning.

I identified what makes some introductory AI knowledge “troublesome.” Students may not know that machines can “learn”. Students may not understand the different ways machines learn. Yes, at this point I could put up some complex math equations that explain machine learning, but what does this mean to a middle school student trying to learn the basics in a visual and tactile manner?

Defining Success and Struggling in Learning

Working backwards, we can define what concept we want to and things we can observe that shows that they have mastered the concept or not. These learning objectives should state clearly what we want the student to learn, by when (e.g. end of class), and what’s the observable way of telling they learned it. A concept in AI that I want students to learn is what is unsupervised machine learning classification versus supervised machine learning classification. They are struggling if they can’t identify what it is visually.

Modeling the Knowledge Using Analogies, Sketches, Data Physicalization, or Stories

To ask myself how I could model this knowledge, I thought of analogies to unsupervised/supervised machine learning classification. I thought of analogies, metaphors, similies, and stories and drew sketches. I won’t list them here, but it involved me drawing sketches with stick figures and drawing what I know about this topic. I then brainstormed ideas about how to physically show the data as it flows through a machine learning classifier or neural network, or some other teaching tool or experimental model. I picked one of the example ideas and decided I would make a simple “maker” exercise for students to try. Hence, the “contraption” I made in the picture with a tube, holes, and small BB and marble-sized balls of different colors. The fun part of this process is that the instructor gets to “make” a low-fidelity prototype proof of concept that will guide what the instructor will then instruct the students to “make,” but not necessarily the same prototype. In other cases, the instructor’s prototype will be the basis of the learning activity the students use. For example, one of the faculty, Mark Ryan, created a prototype game to teach “for” and “if” loops for non-computer scientists.

Prompting the Student to Make Prototypes and Use them to Assess their Learning

After explaining the concept of unsupervised/supervised machine learning classification, I would prompt the student to make something that demonstrates the concept. I wouldn’t want to give them the answer but be there to give them hints and clues and positive encouragement to think of analogies and metaphors themselves. I would instruct and encourage them to use the low-fidelity prototype materials (a.k.a. craft supplies) to build their prototypes and test them on other students. If I’m being kind of vague, it’s because I want to try this out on some of our students this fall to see what I learn first.

Innovating throughout our Engineering Curriculum

I am grateful for the many teaching innovations that I have been able to experience and learn through workshops like Stanford’s d.School’s Teaching and Learning Studio and the KEEN Network’s MakerSpark I just went through in Boston, which I learned these concepts through that I’m able to share. As an engineering leader, I’m grateful that the Kern Family Foundation provides these opportunities for all of our faculty to learn to innovate in their classrooms from other faculty in the KEEN Network. The opportunity is there, and it’s up to us to seize them and make them a reality in our students’ learning experiences.

Picture: A low-fidelity, hands-on teaching model for students to use data physicalization and making to learn the concept unsupervised and supervised machine learning classification.

© 2023 Andrew B. Williams

About the Author: Andrew B. Williams is Dean of Engineering and Louis S. LeTellier Chair for The Citadel School of Engineering. He was recently named on of Business Insider’s Cloudverse 100 and humbly holds the designation of AWS Education Champion. He sits on the AWS Machine Learning Advisory Board and is a certified AWS Cloud Practitioner.  Andrew has also held positions at Spelman College, University of Kansas, University of Iowa, Marquette University, Apple, GE, and Allied Signal Aerospace Company.  He is author of the book, Out of the Box: Building Robots, Transforming Lives.