# Calibrated Q-Matrix-Enhanced Deep Knowledge Tracing with Relational Attention Mechanism
## 6 Conclusions and Future Work  
This paper proposed the novel frameworks, the RAKT model and the QRAKT model. First, relation modeling, including exercise relation modeling, time-order forgetting behavior modeling, and student behavior modeling, was designed to incorporate the relationship of exercises, temporal features, and student behaviors. Second, the positional attention layer and the relational attention layer included relation modeling and exercise content for the KT task. Thirdly, the extension of RAKT, QRAKT, was based on a calibrated Q-matrix generated by a novel hierarchical knowledge-level-based Q-matrix calibration method. Lastly, the effect of the QRAKT model was validated on two public educational datasets, ASSIST2012 and Eedi, in terms of the ACC and AUC. The knowledge state evolution description of a student was conducted to track the student's knowledge state, and an ablation experiment was designed to find the influence of crucial components.  
A significant issue in research in the field of data mining for education is knowledge tracing. Traditional knowledge tracing methods do not take the relation modeling and hierarchical knowledge levels of KCs into consideration. This work improved the performance of the traditional knowledge tracing model in terms of the _Accuracy_ and _Stability_. The online educational system, which applies the QRAKT model, can more accurately track the students' knowledge state and recommend a more personalized and targeted practice. However, the QRAKT model and the RAKT model still have some limitations. The first limitation is that the QRAKT model and the RAKT model only consider the relationship between exercises and knowledge concepts. However, the relationship between students, exercise, and skills is ignored. The second limitation is that this work only considered the hierarchical levels of knowledge concepts. The deeper relationship of knowledge concepts such as relevance or belonging was not considered. Therefore, more work on those problems needs to be performed in the future.  
Figure 9: The comparison of the knowledge tracing result for Skill 49 based on the calibrated Q-matrix relational attention knowledge tracing model (QRAKT), the DKT model, and the DKT+ model. The QRAKT model outperformed the other two models with around a 65% Accuracy on Skill 49. Compared with the performance of the QRAKT model, the DKT model and the DKT+ model performed poorly with around a 48% and a 53% Accuracy on Skill 49.  
**Author Contributions:** Methodology research, L.L. and Z.W.; model realization, L.L.; supervision Z.W.; writing and editing, L.L. and Z.W.; data collection, L.L. and Z.W.; model evaluation, L.L. and Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research work of this paper was supported by the National Natural Science Foundation of China (Nos. 62177022 and 61901165), AI and Faculty Empowerment Pilot Project (No. CCNUA&FE2022-03-01), the Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province (No. xtzd2021-005), and the National Natural Science Foundation of China (No. 61501199).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.