Teacher-Guided Peer Learning With Continuous Action Iterated Dilemma Based on Incremental Network

Abstract

This article proposes a teacher-guided peer learning approach that employs a continuous action iterated dilemma (CAID) model based on an incremental network. Traditional peer learning approaches often assume static communication relationships between students, which is not consistent with actual society, and this affects the effectiveness of peer learning. Additionally, every student is a highly unique individual, and using a single mathematical model to mimic their behavior would result in research findings with limited applicability. Therefore, this article presents several innovations. First, we propose an incremental network generation algorithm that generates an effective communication network to improve classroom efficiency by enhancing the convergence of information between classmates. Second, considering the multiple unknown nonlinear environmental impacts, we design a student dynamic model based on CAID with multiple layers of nonlinearity to fit the different environmental impacts that different students receive. Finally, based on the incremental network and student dynamic model, we design the Lyapunov function to prove the convergence of the proposed model. This mathematical proof ensures that the proposed model is stable and unaffected by parameters, making it more applicable.