## 5 Experimental
### Experimental Results and Analysis
#### 5.3.2 Early Risk Prediction  
To validate the performance of the MTAPSP model in terms of binary prediction performance, three validation methods were used in this study based on relying only on students' clickstream data and assessment score information:  
1. The WF-PD task, which categorizes \"Distinction\" and \"Pass\" as one category and \"Fail\" and \"Withdrawn\" as another. The purpose of the WF-PD task is to identify whether a student has passed the course. 2. The F-PD task, which identifies students at risk of \"Fail\" by placing \"Fail\" in a separate category and \"Distinction\" and \"Pass\" in the same category, excluding dropouts. \"The F-PD task identifies students at risk of \"Fail\". 3. The W-PD task, with \"Withdrawn\" in a separate category and \"Distinction\" and \"Pass\" combined in one category, excluding \"Fail\" students. \"Fail\" students were excluded, to distinguish between students at risk of dropping out and those not at risk. To demonstrate the early risk prediction capability of the MTAPSP model, this paper divided the dataset by percentage of course length and created two models to predict student performance: (1) the \"daily\" model using student activity data as input daily; (2) the \"weekly\" model using students' weekly activity data as input. A comparison was made with the latest student performance prediction model, TEnc [24], and the results of the comparison are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Stages and Performance (\%)**} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Models**} & \multicolumn{2}{c}{**20\%**} & & & & & & & & & \\ \hline \multicolumn{1}{c}{} & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline \multicolumn{1}{c}{} & **Fnc** & WF-PD (daily) & 75.41 & 71.50 & 82.83 & 80.91 & 88.23 & 86.77 & 90.78 & 89.93 & 93.42 & 93.01 \\ \multicolumn{1}{c}{} & **F-PD** (daily) & 91.79 & 91.80 & 92.50 & 92.50 & 92.70 & 92.70 & 93.51 & 93.51 & 94.63 & 94.63 \\ \hline \multicolumn{1}{c}{} & **F-PD** (daily) & 77.80 & 52.50 & 82.95 & 67.70 & 87.04 & 75.92 & 89.29 & 81.04 & 91.85 & 86.22 \\ \multicolumn{1}{c}{} & **F-PD** (daily) & 91.38 & 91.18 & 91.40 & 91.20 & 91.53 & 91.32 & 91.87 & 91.78 & 92.71 & 92.55 \\ \hline \multicolumn{1}{c}{} & **F-PD** (daily) & 83.17 & 68.03 & 90.49 & 83.84 & 94.46 & 90.77 & 96.78 & 94.80 & 98.02 & 96.88 \\ \multicolumn{1}{c}{} & **F-PD** (daily) & 95.73 & 95.71 & 96.08 & 96.07 & 97.28 & 97.27 & 98.08 & 98.08 & 99.08 & 99.08 \\ \hline \multicolumn{1}{c}{} & **F-PD** (weekly) & 76.76 & 73.37 & 83.93 & 81.87 & 88.96 & 87.62 & 91.81 & 91.10 & 93.57 & 93.21 \\ \multicolumn{1}{c}{} & **F-PD** (weekly) & 91.02 & 91.03 & 91.99 & 91.99 & 92.59 & 92.59 & 93.17 & 93.18 & 94.32 & 94.33 \\ \hline \multicolumn{1}{c}{} & **F-PD** (weekly) & 78.53 & 55.68 & 83.73 & 69.20 & 87.85 & 77.41 & 90.43 & 83.09 & 91.75 & 86.27 \\ \multicolumn{1}{c}{} & **F-PD** (weekly) & 91.00 & 90.77 & 91.17 & 91.00 & 91.15 & 91.01 & 91.95 & 91.83 & 92.71 & 92.54 \\ \hline \multicolumn{1}{c}{} & **F-PD** (weekly) & 83.79 & 70.63 & 91.01 & 84.79 & 95.51 & 92.61 & 97.22 & 95.58 & 98.14 & 97.10 \\ \multicolumn{1}{c}{} & **F-PD** (weekly) & 94.46 & 94.43 & 96.10 & 96.09 & 96.85 & 96.84 & 97.94 & 97.94 & 98.92 & 98.92 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of prediction performance for model binary classification. An analysis of Table 6 reveals the following:  
1. The proposed method in this paper achieved optimal performance across various course durations, demonstrating the superior effectiveness of MTAPSP in binary predicting student performance. 2. Compared with the TEnc model, the three-layer LSTM network owned by MTAPSP performs better in dealing with long sequence data, indicating that the three-layer LSTM is more adept at capturing the patterns of long sequence data. 3.