Personas-based Student Grouping using reinforcement learning and linear programming

Group discussions and assignments play a pivotal role in the classroom and online study. Existing research has mainly focused on exploring the educational impact of group learning, while the study on automated grouping still remains under-explored. This paper proposes a principled method that aims to achieve personalized, accurate, and efficient grouping outcomes. Dubbed as Personas-based Student Grouping (PSG), our method first applies unsupervised clustering techniques to assign personas to students based on their behavioral characteristics. Based on their personas, we then utilize deep reinforcement learning to search for appropriate grouping rules and perform linear programming to obtain a suitable grouping scheme. Finally, the teaching effectiveness is fed back as the rewards to the reinforcement learning model to optimize future grouping scheme selections. Extensive experiments conducted on MOOCs datasets show that PSG can achieve more advantageous performance in both efficiency and effectiveness compared to the manual or random grouping mechanism. We hope PSG can provide students with a more enhanced learning experience and contribute to the future development of education. Our project homepage is available at https://PSG-project.pages.dev.