Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies

Effective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members. However, the current approaches to collaborative learning grouping often lack a thorough examination of students' knowledge-level characteristics, thereby failing to ensure that the knowledge structures of group members complement each other. Therefore, a collaborative learning grouping method incorporating the optimization strategy of deep knowledge tracking is proposed. Firstly, the optimized deep knowledge tracking (DKVMN-EKC) model is used to model the knowledge state of learners to obtain the degree of knowledge mastery of learners, and then the K-means method is used to similarly cluster all learners, and finally, the learners of different clusters are assigned to suitable learning groups according to the principle of heterogeneity of grouping. Extensive experiments have demonstrated that DKVMN-EKC can precisely model students' knowledge mastery levels and that the proposed approach facilitates effective grouping at the level of students' knowledge structures, thereby ensuring fairer and more heterogeneous grouping results. This approach fosters positive interactions among students, enabling them to learn from one another and effectively improve their understanding of various knowledge points.