# Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies
## 6 Conclusions and Future Work  
To address the problem that the current collaborative learning grouping approaches lack deep calculations of students' knowledge level characteristics, we propose a collaborative learning grouping approach that incorporates deep knowledge tracing optimization strategies. Particularly, we introduce a Q matrix to the DKVMN model and consider students' current knowledge state in the knowledge increments. We design the DKVMN-EKC model, and use the proposed deep knowledge tracing model to explore the deep connotations behind students' test scores and obtain students' knowledge mastery level to perform similarity clustering of students. Subsequently, we assign students within a cluster to different learning groups and conduct an empirical study to demonstrate the effectiveness of the proposed grouping approach. The incorporation of the knowledge tracing model focuses collaborative learning grouping on students' cognitive states and realizes learning group division on this basis; conversely, it addresses the existing problem of simplifying the modeling of the knowledge level characteristics of collaborative learning grouping, and on the other hand, grouping from the perspective of the knowledge composition structure can effectively enhance positive interactions among students [30]. Although the grouping approach proposed in this study has achieved some success, there are still areas for improvement. While the proposed grouping approach demonstrated effectiveness in this study, the small sample size (n = 30) and the homogeneous nature of the vocational student population limit the generalizability of the results. The study's findings are specific to a particular educational context and may not be directly applicable to other settings. To address this limitation, future research should involve larger, more diverse student populations across different educational stages (e.g., secondary schools and universities) and disciplines (e.g., humanities and sciences). Additionally, the current study's sample size was determined by the availability of participants in the specific vocational program, which constrained the statistical power of the analysis. Future studies should aim for larger sample sizes to enhance the reliability of the results. For instance, a sample size of at least 100 students would allow for more robust statistical testing and better representation of diverse learning profiles. Furthermore, the study's sample was limited to a specific region, which may not reflect the broader demographic and educational diversity found in other regions. Future research should include participants from multiple geographic locations and cultural backgrounds to assess the cross-cultural validity of the proposed method. To ensure the robustness of the findings, future studies should also employ a mixed-methods approach, combining quantitative data with qualitative insights from student interviews and teacher observations. This would provide a more comprehensive understanding of the impact of the grouping approach on collaborative learning outcomes. In summary, while the current study provides a promising foundation, its conclusions should be interpreted with caution due to the small and homogeneous sample. Future research with larger, more diverse samples and cross-cultural validation is needed to fully establish the generalizability of the proposed method. Conceptualization, H.L. and Y.C. ; methodology, H.L. and Y.C. ; software, W.L. ; validation, X.W., W.L. and Y.C. ; formal analysis, H.L. ; investigation, Y.C. ; resources, H.L. ; data curation, W.L. ; writing--original draft preparation, Y.C. ; writing--review and editing, W.L. ; visualization, X.W. ; supervision, H.L. ; project administration, H.L. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.