## 4 Experiment
### Comparative Experiment  
To validate the efficacy of the proposed multi-scale feature-based AES method, we conducted a comparison with the baseline methods listed in Table 3.  
1. **EASE (SVR)**[42]: Enhanced AI Scoring Engine (EASE) is a public machine-learning-based classification scoring engine ([[https://github.com/openedx-unsupported/ease](https://github.com/openedx-unsupported/ease)]([https://github.com/openedx-unsupported/ease](https://github.com/openedx-unsupported/ease)) (accessed on 23 May 2023)). As EASE relies on hand-engineered features and regression techniques, we adopt the Support Vector Regression (SVR) model as the baseline approach for comparison purposes in this paper. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Prompt**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **Ave.** \\ \hline EASE(SVR) & 0.781 & 0.621 & 0.630 & 0.749 & 0.782 & 0.771 & 0.727 & 0.534 & 0.699 \\ LSTM-MoT & 0.775 & 0.687 & 0.683 & 0.795 & 0.818 & 0.813 & 0.805 & 0.594 & 0.746 \\ CNN(10 runs) + LSTM(10 runs) & 0.821 & 0.688 & 0.694 & 0.805 & 0.807 & 0.819 & 0.808 & 0.644 & 0.761 \\ CNN-LSTM-ATT & 0.822 & 0.682 & 0.672 & 0.814 & 0.803 & 0.811 & 0.801 & 0.705 & 0.764 \\ SkipFlow LSTM & 0.832 & 0.684 & 0.695 & 0.788 & 0.815 & 0.810 & 0.800 & 0.697 & 0.764 \\ TSLF & **0.852** & 0.736 & 0.731 & 0.801 & 0.823 & 0.792 & 0.762 & 0.684 & 0.773 \\ Tran-BERT-MS-ML-R & 0.834 & 0.716 & 0.714 & 0.812 & 0.813 & **0.836** & **0.839** & **0.766** & 0.791 \\ MSSF & 0.846 & **0.748** & **0.737** & **0.820** & **0.826** & 0.825 & 0.820 & 0.725 & **0.793** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the performance of the present models with that of the state-of-the-art models on ASAP. The best performance for each prompt is highlighted in bold. 2. **LSTM-MoT**[19]: Treating the essay text as a word sequence, we use the LSTM network to extract the temporal relationship and average the output of all time step states for the final scoring. 3. **CNN(10 runs) + LSTM(10 runs)**[19]: By employing an integrated learning approach, ten CNN and ten LSTM models were utilized for prediction, with the resulting predictions being averaged to yield the final prediction outcome. 4. **CNN-LSTM-ATT**[21]: We use CNN and the attention mechanism to extract sentence-scale features of the essay; then, we input the result into the LSTM model and perform the final scoring through the attention mechanism. 5. **SkipFlow LSTM**[43]: To enhance the performance of essay scoring, the model incorporates the SkipFlow mechanism into the LSTM network. This mechanism leverages the semantic relationships between the hidden layers of the LSTM as auxiliary features. 6. **TSLF**[29]: We use LSTM to obtain semantic features, consistency features, and semantic relevance features, combined with features, such as grammatical errors. We then use XGBoost to score the essay. 7. **Tran-BERT-MS-ML-R**[26]: Two BERT models are used to extract token-scale, document-scale, and segment-scale features; multiple losses are used for essay scoring. The above table shows the proposed multi-scale essay scoring model and the baseline model for comparison in this paper and the experimental results show that: (1) The prediction of the final score by manually extracting features and then utilizing traditional machine learning algorithms is not effective, indicating that relying on shallow linguistic features alone to characterize the whole essay will miss the deep semantic meaning of the essay, which is very fatal in the process of scoring essays. Secondly, parameters such as the kernel function and penalty parameters of SVR have a significant impact on performance; however, it is difficult to determine the optimal value and it is more difficult to expand the future than the DNN-AES method.