# Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies
## 1 Introduction  
Collaborative learning, as a pedagogical cornerstone, is deeply rooted in social constructivism--a theory positing that knowledge is co-constructed through social interactions and shared problem-solving. This approach transcends unidirectional knowledge transfer from teachers to students, fostering multi-dimensional exchanges among learners and instructors. Over decades, the efficacy of collaborative learning has been empirically validated [1, 2], with studies demonstrating its role in enhancing theoretical proficiency, innovation, and practical application skills [3]. Recent advancements in cognitive science further elucidate the mechanisms underlying collaborative learning. The cognitive load theory suggests that distributing complex tasks among group members mitigates individual cognitive overload, thereby optimizing knowledge retention. Concurrently, distributed cognition frameworks emphasize that knowledge states emerge collectively through group interactions rather than residing solely within individuals. These theoretical foundations align with modern AI-driven tools, such as deep knowledge tracing (DKT) models [4], which dynamically map learners' evolving competencies across shared knowledge domains. However, to ensure that collaborative learning can be conducted effectively, it is crucial to assign each student to an appropriate learning group. Research consistently demonstrates that the effectiveness of group learning with a good organizational structure is considerably higher than that of arbitrary groups or individuals [5; 6; 7]. Currently, collaborative learning groups are typically grouped manually or through the application of automatic matching algorithms [8]. However, these two approaches are relatively simplistic in modeling the features of students' knowledge levels, neglecting to consider the vast diversity in students' knowledge compositions. Consequently, they cannot ensure the complementarity of the knowledge structures of members within the group. Even among students with the same level of achievement, there may exist notably distinct knowledge structures, and an accurate assessment of students' knowledge states becomes paramount, as it can help uncover individual weaknesses and promote efficient communication amongst learners. Knowledge tracing is a mainstream approach to diagnosing students' knowledge states, and it can be used to obtain students' cognitive states through their response data and diagnose their mastery of each knowledge point. Knowledge tracing provides a more efficient approach for accurately diagnosing students' knowledge states and offers the possibility of further improving the complementarity of knowledge structures among group members. In this study, to achieve the complementary grouping of knowledge structures, we conducted the following steps:  
* We considered students' knowledge mastery levels in the grouping process of collaborative learning and presented an innovative approach that incorporated deep knowledge tracing optimization strategies. The approach included three key modules: a data processing module, a knowledge state diagnosis module, and a student grouping module. * A dynamic key-value memory network with exercise knowledge characteristics (DKVMN-EKC) model was proposed to improve the existing dynamic key-value memory network (DKVMN) model. DKVMN-EKC effectively represents students' actual knowledge points and precisely evaluates their mastery levels of knowledge points. * Considering the knowledge level of the students, the efficacy of the grouping approach was rigorously evaluated by comparing multiple groups of experiments.