## 5 Experiments
### Comparison with SOAT Hierarchical Clustering Methods Based on the UIWP Dataset  
In this section, we evaluate the advanced nature of our proposed methodology in the domain of learning task-structured representation through comparative experiments with SOAT methods. Selected methods for comparison include hierarchical clustering methods like Bayesian Hierarchical Clustering (BHC) [42], Min-Min-Roughness (MMR) [43], and Bayesian Rose Tree (BRT) [30]. A commonality between these methods and our proposed approach is their capability to construct hierarchical representations for learning task-planning. To ensure fairness and validity in the comparative analysis, all methods utilize SAL features consistent with those presented in Table 3 wherever possible. During the prediction phase, all of these hierarchical clustering methodologies employ the same Tree-LSTM model and undergo parameter optimization through identical procedures.  
As illustrated in Table 3, it is evident that the methodology proposed in this study demonstrates a superior performance over the baseline methods across multiple evaluation metrics. Specifically, the proposed approach surpasses the best-performing baseline method by approximately 7.1% in terms of average prediction accuracy for the UWP dataset. Notably, our method's performance exceeds that of the original BRT model, thereby substantiating the efficacy of the proposed model in the learning process. Moreover, among all methods, the BHC method exhibits the weakest predictive performance. This can be attributed to the fact that the binary tree structure is not congruent with the structural nuances of the learning process. In Table 4, the confusion matrix corresponding to the method we have proposed is delineated. This matrix effectively illustrates the performance of our methodology in terms of true positives, false positives, true negatives, and false negatives. Through this representation, we aim to provide a clear and comprehensive understanding of the accuracy, precision, recall, and specificity of our approach.  
Further, we conducted a comparative analysis of various algorithms' predictive capabilities across learning tasks with varying numbers of subtasks. As illustrated in Figure 5  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **TP** & **FP** & **TN** & **FN** \\ \hline Our method & 104 & 13 & 91 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The confusion matrix our method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1** \\ \hline BHC & 0.717 & 0.701 & 0.709 \\ MMR & 0.782 & 0.73 & 0.755 \\ BRT & 0.82 & 0.805 & 0.812 \\ Our method & 0.889 & 0.825 & 0.856 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results with hierarchical clustering methods.  
(where the X-axis represents the number of subtasks in a learning task), with an increase in the number of subtasks, the prediction accuracy of the method proposed in this paper declines less compared to that of other baseline methods. When the number of tasks reaches 14 (which corresponds to the assignment with the most subtasks in this course), the difference in prediction accuracy between our proposed method and the best-performing baseline algorithm is at its maximum. In summary, as the number of subtasks increases, the performance advantage of our proposed algorithm becomes increasingly evident.