Using Q-Learning to Personalize Pedagogical Policies for Addition Problems

The prevalence of COVID-19 has illuminated the need for practical digital education tools over the past year. With students studying from home, teachers have struggled to provide their students with adequately challenging coursework. Our project aims to solve this issue in the context of math. More specifically, our goal is to encourage thoughtful learning by supplying students with personalized two-number addition problems that take time to solve but expect the student to answer correctly still. Our solution is to model the process of selecting a math problem to give a student as a Markov Decision Process (MDP) and then use Q-learning to determine the best policy for arriving at the most optimally challenging two-number addition problem for that student. The project creates three student simulators based on group member data. We show that it took student one: $(162 \pm 134)$ iterations to give appropriate level problems where the first entry is mean and the second is the standard deviation. Student two took $(230 \pm 205)$ iterations, and student three took $(247 \pm 236)$ iterations. Lastly, we demonstrate that pre-training our model on students two and three and testing on student one showed a significant improvement from $(162 \pm 134)$ iterations to $(35 \pm 44)$ iterations.