## 4 Results and Discussion  
### Micro-Features, Aggregated Features, and Their Combination in Characterizing Writing Quality  
In the investigation of the power of different types of features for assessing writing quality, three distinct groups were utilized: micro-features, aggregated features synthesized from the averaged combinations of micro-features, and a comprehensive approach incorporating both micro-features and aggregated features. The selection criteria for linguistic features and components to be included in the further analysis were based on their demonstrated significant correlation with writing quality, accompanied by at least a small effect size (\(r>0.100\)) [4]. To assess the influence of these different feature types on the evaluation of writing quality, we conducted experiments on the aforementioned six models using a validation dataset. We also delved into the influence of different feature types across holistic and analytic rating dimensions: _Ideas_, _Organization_, _Style_, _Conventions_, and _Holistic_. The analysis encompassed three scenarios: utilizing the qualified micro-features only (194 features), exploring these seven aggregated features generated by PCA only, and examining a combination of both. To better examine the difference in characterizing writing quality, we computed the mean of the evaluation metrics of the six methods for comparative analysis to facilitate the comprehension of how micro-features, aggregated features, and their combination contribute to the prediction of writing quality. Table 4 indicates the effectiveness of different feature sets: micro-features only, aggregated features from the averaged combinations of micro-features, and their combination in characterizing writing quality. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Evaluation Metrics** & **Aggregated Features Only** & **Micro-Features Only** & **Combined** \\ \hline R\({}^{2}\) & 0.517 & 0.685 & **0.690** \\ QWK & 0.705 & 0.820 & **0.822** \\ Exact Agreement & 0.225 & **0.288** & 0.285 \\ Exact-Plus-Adjacent Agreement & 0.470 & **0.560** & 0.555 \\ \hline \hline \end{tabular}  
* The most optimal results are highlighted in bold for clarity and emphasis.  
\end{table} 
Table 4: Comparison between micro-features, aggregated features, and a combination of both. The coefficient of determination \(R^{2}\) showed that the combined feature approach yielded the highest value (\(R^{2}\) = 0.690). This indicated a marginally superior fit to the data when compared to using micro-features (\(R^{2}\) = 0.685) alone and a notably better performance over aggregated features (\(R^{2}\) = 0.517). Paired \(t\)-tests were conducted to explore whether significant improvement exists. The results showed that the micro-feature (\(p<0.050\)) and combined feature approaches (\(p<0.050\)) were significantly improved when compared with the aggregated feature approach. However, no significant differences were found between the micro-feature and combined feature approaches (\(p>0.050\)). In terms of QWK, the results also supported the superiority of the combined features model with a score of 0.822, marginally outperforming the micro-features model (0.820) and significantly surpassing the aggregated features model (0.705). This increment, although slight, indicated that there were no significant differences between the micro-feature approach and the combined feature approach (\(p>0.050\)). As for Exact Agreement and Exact-Plus-Adjacent Agreement, a slight advantage for micro-features in characterizing writing quality has been reported when compared with the combined feature approach (\(p>0.050\)). Overall, similar results have also indicated that the micro-feature and combined feature approaches provided a robust basis for characterizing writing quality when compared with the aggregated feature approach [4]. Additionally, the results demonstrated no differences between AES models with micro-features and AES models combining micro-features and aggregated components (\(p>0.50\)).