# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 6 Results and Analysis
### Student Performance Prediction Results and Analysis (**Rq1**)  
Due to the challenge of directly measuring learners' knowledge states, we adopt the performance prediction task as a proxy to assess the accuracy of KT models in estimating these states, following prior research. To the efficacy of LBAKT, we conduct a comparative analysis against various baseline models in predicting learners' performance and present the experimental findings in Table 3. The performance of KT models is evaluated using the metrics Area Under Curve (AUC) and Accuracy (ACC) in all experiments. AUC, the area under the receiver operating characteristic (ROC) curve, is a graphical representation of the trade-off between true positive rate and false positive rate for a binary classification model. The AUC value ranges from 0 to 1, with higher values indicating better performance. ACC represents the ratio of correctly predicted instances to the total instances in the datasets. Based on Table 3, in the comparative analysis of student performance prediction models, LBAKT consistently demonstrates superior results on the ASSIST2012 and ASSISTChall datasets. Specifically, LBAKT outperforms other methods with an AUC of 0.77414 and an ACC of 0.76072 on ASSIST2012, reflecting a 4.98% higher AUC and a 4.8% higher ACC than DKT, the baseline model. On ASSISTChall, LBAKT achieves an AUC of 0.81037 and an ACC of 0.75307, surpassing DKT by 8.8% in AUC and 3.3% in ACC. LPKT performs slightly worse than LBAKT on the ASSIST2012 and ASSISTChall datasets. DKT+ shows dominance in the Algebra0607 dataset, obtaining the highest AUC 0.81104 and ACC 0.80950, marking an approximately 3.16% improvement in AUC and 5.0% in ACC over LBAKT. These results suggest that LBAKT is more effective for a longer learning sequence; while the Algebra0607 dataset records the steps of the students in answering the exercises, when we treat each step of each exercise as a separate exercise, the predictive performance of our model LBAKT is relatively low because directly using steps as independent exercises cannot fully utilize the associative information between exercises. In contrast, except LPKT, all baseline models which only consider question and answer, do not utilize more auxiliary information and show higher predictive performance. The varying performance across datasets emphasizes the importance of selecting appropriate models based on dataset characteristics, highlighting the adaptability of the proposed methods to different educational scenarios. The improvement margins observed in the experiments highlight the effectiveness of the LBAKT model, particularly in terms of handling longer learning sequences. To provide a more comprehensive assessment of the comparative predictive performance of the models, we executed the training for a total of 100 epochs on ASSISTChall dataset. The results are shown in Figure 2. This extended training period allows for a thorough evaluation of how each method converges and stabilizes in its predictive accuracy over an extensive learning process. In Figure 2, it is shown that while the trajectory curves of all models are broadly similar, the predictive performance of the DKT, DKT+, DKVMN, and SAKT algorithms significantly lags behind that of LPKT and LBAKT. This difference is attributed to the fact that only the LPKT and LBAKT methods take into account factors such as forgetting and students response time, which more closely align with the human learning process. Under identical conditions, the LBAKT model achieves superior predictive performance. LPKT is a model that demonstrates good predictive performance in long learning sequences. To validate that our model, LBAKT, also has this capability, we conducted comparative experiments between learning sequence length and predictive performance. Figure 3 shows the comparison results of the influence of learning sequence length of LBAKT and LPKT on ASSISTChall. We set the learning sequence lengths to four different values: 200, 100, 20, and 10.