## 4 Results and Discussion
### Most Powerful Linguistic Features per Rating Trait: Visualization via SHAP Model  
To identify the most powerful linguistic features per rating trait, we calculated SHAP values to offer a rigorous quantification of each feature's impact on the model's prediction, thereby elucidating the relative importance of various linguistic features in determining different constructs of writing quality. Figures 6 and 7 encapsulate the top 10 influential features for the holistic and analytic rating traits. Detailed information of linguistic features can be found in Appendix A. The variability in the impact of each feature, as indicated by the spread of SHAP values, underscores the complex interplay between different linguistic attributes when assessing writing quality. The insights derived from this analysis can inform educational interventions, automated writing assessment tools, and further research into the features that contribute to effective writing. Figure 6 illustrates the SHAP values associated with the top 10 linguistic features that characterize writing quality at the holistic rating trait, thereby offering insights into the relative importance of these linguistic features. It was observable that certain features such as (hdd42_cw) and (nsentences) predominantly had a positive SHAP value distribution, indicating that higher values of these features typically correspond to better writing quality. In contrast, (Freq_N_CW) exhibited a predominantly negative distribution of SHAP values, suggesting that higher values of these features may be associated with lower writing quality. The results offered support to previous findings that less proficient writers tended to utilize more high-frequency words compared to their more proficient counterparts [15, 55]. The features were arranged in descending order of their mean absolute SHAP values, signifying their average impact across the dataset. _HD-D_ (hdd42_cw), measuring the lexical diversity, had the highest mean absolute SHAP value, indicating it was the most influential in the model's assessment of writing quality. Additionally, lexical diversity indices, including the measure of textual lexical diversity (mtld_ma_bi_aw & mtld_original_cw) were identified as significant linguistic features for expository writing quality. The findings corroborated earlier research demonstrating that features based on the type-token ratio were key indicators of writing quality, as substantiated by [65, 67, 105]. Figure 6: SHAP values: visualization of the top 10 features impacting the holistic rating trait. Other features including the number of sentences (nsentences), number of words (nwords), word frequencies (Freq_HAL_CW), LSA-based indices (lsa_all_fwd & lsa_2-3_rwd), mean phonological Levenshtein distances (PLD), and neighborhood frequency of content word (Freq_N_CW) were identified as predictive linguistic features for the overall writing quality of exposition. The results offered support to previous studies showing that LSA-based features [81,82] and word frequencies [15,55] are significantly correlated with writing quality. With the XAI technique (SHAP), thorough and transparent linguistic features characterizing the overall writing quality of exposition are provided, which enhances the explainability of the proposed AES model and offers diagnostic and individualized feedback for multi-dimensional writing instruction and assessment. In terms of _Ideas_, SHAP values were plotted on the horizontal axis, quantifying the degree to which each feature affected the predictive model's output (see Figure 7a). The vertical listing of features indicated their relative importance based on the magnitude of their mean absolute SHAP values, with number of words (nwords) appearing as the most predictive feature. Upon examination of the plot, it was apparent that number of words (nwords) was associated with higher SHAP values, suggesting that it was positively related to writing quality for the _Ideas_ dimension.