A deep reinforcement learning approach to automatic formative feedback

Receiving formative feedback about open-ended responses can facilitate the progression of learning. However, educators cannot often provide immediate feedback and thus for students, learning may be slowed. In this paper, we will explore how an automatic grading model can be coupled with deep Reinforcement Learning (RL) to create a system of automatic formative feedback for students' open-ended responses. We use batch (offline) learning with a double Deep Q Network (DQN) to simulate a learning environment, such as an open-source, online tutoring system, where students are prompted to answer open-ended questions. An auto-grader is used to provide a rating of the student's response, and until the response is scored at the highest category, an RL agent iteratively provides suggestions to the student to revise the previous version of their answer. The automated suggestion can include either a key idea to consider adding to the response, or a recommendation to delete a specific part of the response. Our experiments are based on a simulated environment, within which we anticipate a how a real student might revise their answer based on the agent's chosen hint. Preliminary results show that in such environment, the agent is able to learn the best suggestions to provide a student in order to improve the student's response in the least number of revisions.