What Happens Next?

As I approach the end of my time at Michigan State University, I reflect back on what I’ve learned, and I transition my mindset towards figuring out what to do next. I’m a strong believer in lifelong learning. Even though I’ve been enrolled in MSU as a full-time student, I’ve also taken a handful of other courses through online platforms such as Coursera, edX, and Udacity. I know that when I graduate with my Master’s degree this spring, my learning at MSU will finish, but my online learning will continue on. My big goal that I have been working towards for the last couple years has been to create adaptive math games for education. Adaptive difficulty, differentiated instruction, ongoing formative assessments, and individualized feedback, all existing within a game’s framework. In order to build an adaptive math game, three large domains need to come together: math education, game design/development, and machine learning/data analysis. I spent my undergraduate years learning new and inventive ways to teach students math, which I successfully utilized in my own classrooms as an 8th grade math teacher. However, I do not know how to make games, and my knowledge in making predictions with data is still largely underdeveloped. That is why I want to continue learning more about how to build and implement games, while also dipping my toes into the world of big data and computer science.

As I’ve been taking classes at Michigan State University, I’ve started realizing that while half of the challenge is building a great educational game, the other half of the challenge is getting teachers to use them successfully in classrooms. That’s why I need to learn more about how to best integrate math games into classrooms. What materials should the teacher be provided such that they are more likely to utilize the resource? What should the teacher’s role be if students are playing games in the classroom? How can I make accessibility as straightforward and easy as possible? While I think that perhaps one of the best ways to learn about successful implementation comes from trial and error in actual classrooms with real teachers and students, a more immediate/practical solution for learning these things is through online courses. For instance, upon graduation at MSU, I will enroll in this edX program offered through MIT (Massachusetts Institute of Technology): Educational Technology XSeries Program.

Part of building an adaptive math education game is learning how to make predictions based from data. I need to revisit topics in probability, statistics, linear algebra, and calculus in order to even understand how these predictive algorithms work. Then I need to learn various computer science skills, including SQL (Structured Query Language), TensorFlow, efficient algorithms, and using online servers. Fortunately, I have a degree in mathematics, so revisiting the mathematics concepts will not be very difficult. I am also already enrolled in two programs through Udacity which are teaching me how to create neural networks in TensorFlow. However, in order to deal with the large amount of data used to build a model for prediction, I need to learn SQL. In order to have a program that can modify the game quickly and efficiently, I need to learn how to write better and more efficient algorithms. And finally, in order to have a predictive model that can continuously update as more and more students play the game, I need to learn how online servers work, such that information can be collected through interaction with a game to update how well the model can make predictions. One of the resources I will utilize to get stronger with my programming and algorithmic thinking is this Stanford University program offered through Coursera: Learn to Think Like a Computer Scientist.

Finally, the third area that I need to focus my studies on is being able to create games. There are several alternatives, such as hiring a company to build the game, or re-purposing an existing game. However, in order to utilize very specific theories of math education, and to incorporate the adaptive pieces of the game, it will be necessary to be able to build the game up from scratch. Being able to build games is a huge field on its own. However, there do exist numerous online tutorials and guides for building games in existing game engines, such as Unity, Unreal Engine, or Game Maker Studio. A good starting place for me to learn game development is through this series of Game Maker Studio tutorials: Make Your First Game!

The above resources are just a select few from the many courses, books, and tutorials that I have bookmarked throughout the last year. It’s a lot to learn, but perhaps it will all be necessary if I want to pursue my goal of building adaptive math education games. I have made significant progress in my learning for the past few years, and I foresee many similar years to come. After all, in order to be a successful educator, I believe that I need to be constantly learning and improving.