## 5 Experimental
### Experimental Results and Analysis
#### 5.3.1 Multi-Classification Prediction Performance  
To validate the advantages of the MTAPSP model for multi-classification prediction, four benchmark models, random forest (RF), naive Bayes (NB), deep feedforward neural network (DFFNN) [34], and sequential engagement-based prediction network for academic performance (SEPN) [26], were used for comparative experiments, using the students' clickstream data, their assessment scores, and the data of the students' demographic characteristics, and each of these models' performance is shown in Table 5.  
Table 5 shows that the MTAPSP model obtained the best performance in most cases. Although the RF model performs best in differentiating between \"Distinction\" performance categories, given the imbalance of the data, we must consider evaluation metrics based on F1 scores. The MTAPSP model has slightly higher F1 scores than the RF model for the categories \"Failed\", \"Passed\", and \"Withdrawn\", indicating that the MTAPSP model  
\begin{table}
\begin{tabular}{c c} \hline
**Experimental Environment** & **Environment Configuration** \\ \hline Operating systems & Windows 10 \\ CPU & Intel core i5-6700 HQ \\ Video Cards & NVIDIA GTX 960 \\ RAM & 16 GB \\ DISK & 1T SSD \\ Programming Languages & Python 3.6 \\ Framework & Scikit-learn 0.24.2 + Pytorch 1.10.2 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Precision** & **RF** & **NB** & **DFFNN** & **SEPN** & **MTAPSP** \\ \hline Distinction & 0.66 & 0.24 & 0.66 & 0.40 & 0.58 \\ Fail & 0.59 & 0.34 & 0.55 & 0.55 & 0.62 \\ Pass & 0.78 & 0.62 & 0.74 & 0.71 & 0.79 \\ Withdrawn & 0.73 & 0.72 & 0.76 & 0.74 & 0.78 \\ Macro avg. & 0.69 & 0.48 & 0.68 & 0.60 & 0.69 \\ Weighted avg. & 0.71 & 0.55 & 0.70 & 0.66 & 0.73 \\ \hline
**Recall** & **RF** & **NB** & **DFFNN** & **SEPN** & **MTAPSP** \\ \hline Distinction & 0.54 & 0.57 & 0.47 & 0.07 & 0.40 \\ Fail & 0.43 & 0.37 & 0.36 & 0.29 & 0.51 \\ Pass & 0.89 & 0.454 & 0.90 & 0.96 & 0.89 \\ Withdrawn & 0.77 & 0.65 & 0.80 & 0.83 & 0.83 \\ Macro avg. & 0.66 & 0.51 & 0.63 & 0.54 & 0.66 \\ Weighted avg. & 0.72 & 0.50 & 0.71 & 0.70 & 0.74 \\ \hline
**F1-score** & **RF** & **NB** & **DFFNN** & **SEPN** & **MTAPSP** \\ \hline Distinction & 0.59 & 0.34 & 0.55 & 0.12 & 0.47 \\ Fail & 0.50 & 0.36 & 0.44 & 0.38 & 0.56 \\ Pass & 0.83 & 0.52 & 0.81 & 0.82 & 0.84 \\ Withdrawn & 0.75 & 0.68 & 0.78 & 0.79 & 0.80 \\ Macro avg. & 0.67 & 0.47 & 0.64 & 0.53 & 0.67 \\ Weighted avg. & 0.71 & 0.52 & 0.70 & 0.65 & 0.73 \\ Accuracy & 0.72 & 0.50 & 0.71 & 0.70 & 0.74 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of model multi-classification prediction performance.  
has better predictive power in the face of data imbalance. Compared to the deep learning models DFFNN and SEPN, the MTAPSP model achieved superior results in all metrics, which confirms that it is more capable of capturing temporal features of learning behaviors and integrating multidimensional features to more accurately predict student performance.  
The multi-classification prediction experiments clearly demonstrate the excellent performance of the MTAPSP model in this area. Teachers can predict student performance by utilizing the MTAPSP model and are able to gain insight into the academic level of their students and develop targeted instructional programs accordingly. For instance, students who are predicted to have \"Distinction\" results are recommended higher-level courses to deepen their learning and improve their abilities; students who are predicted to withdraw from school are provided with the necessary support and assistance by finding out whether they have any difficulties in their academic life. The application of the MTAPSP model not only provides an effective tool for predicting student performance but also provides a powerful means of guidance for educators, enabling them to counsel students in a more comprehensive and individualized manner, and promoting the improvement of teaching quality.