# A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance
## 6 Experiment
### Performance Evaluation
In contrast, MASKT utilizes a dynamic weighting mechanism within its multi-noise-processing framework to suppress performance fluctuations; nevertheless, for \(\lambda>0.5\), slight overfitting still occurs. As illustrated in Figure 12b, on the ASSIST09 dataset, optimal AUC performance is achieved for both the filling and delete single-noise-processing methods as well as the complete MASKT model at \(\lambda=0.4\), with AUC improvements of approximately 5% to 7%. At this stage, the dynamic-random method significantly outperforms other single processing approaches, with an AUC gain of roughly 3%. This may be attributed to the dominance of filling noise and delete noise in this dataset, combined with a uniform noise distribution, making the dynamic strategy well-suited to the noise characteristics under the optimal \(\lambda\). In Figure 12c, for the ASSIST12 dataset, when \(\lambda=0.5\), the random method exhibits a temporary improvement in performance, with the AUC increasing by 2%. However, MASKT subsequently shows an unstable downward trend, with the AUC decreasing by approximately 4%. This fluctuation stems from the high proportion of random noise present within the dataset. At \(\lambda=0.5\), the random method overemphasizes sequence  
Figure 11: Comparison of AUC performance of MASKT model dataset. (**a**) Performance on the Algebra05 dataset; (**b**) Performance on the ASSIST2009 dataset; (**c**) Performance on the ASSIST2012 dataset; (**d**) Performance on the ASSIST2015 dataset; (**e**) Performance on the EdNet dataset. correlations, revealing its short-term adaptability. As \(\lambda\) increases further, the excessive dynamic shifting of sequences induced by random noise reduces the overall robustness of MASKT. As shown in Figure 12d, for the ASSIST15 dataset, when \(\lambda>0.5\), the random method exhibits a wavelike decline, with AUC oscillations of approximately \(\pm 3\%\), while the MASKT model maintains relatively stable performance throughout. In summary, the random method demonstrates lower overall stability compared to other noise-processing techniques. Its performance fluctuations align with established cognitive patterns: random strategies may exhibit localized effectiveness in simple or uniformly noisy environments, but struggle to adapt in complex, dynamic real-world scenarios where noise distributions are heterogeneous. Fundamentally, performance fluctuations are driven by the interplay of dataset-specific noise heterogeneity, loss weight sensitivity, and the robustness of the noise-processing mechanism. Future research will focus on introducing quantifiable noise distribution metrics to optimize the selection of \(\lambda\) thresholds, further enhancing both performance and model interpretability. The MASKT model extends the topic embedding module based on the SAKT model to further improve the prediction performance. The overall performance is slightly better than other topic embedding models, and in some datasets, the performance is slightly worse than other models, such as DHKT [24], which is richer in mining features and more comprehensive in topic embedding by introducing semantic dimensional multi-layer attention correlation compared with single skill dimension correlation. MASKT performs best in the ASSIST09 and ASSIST12 datasets, and the difference with the optimal model in other datasets is about 0.2 percentage points, which indicates that MASKT has a high prediction performance while improving the interpretability of the model.