## 4 Results and Discussion
### Most Powerful Linguistic Features per Rating Trait: Visualization via SHAP Model
Conversely, the semantic overlap of function words (lsa_all_fwd) was associated with negative SHAP values, indicating a potential association with lower quality in the _Ideas_ dimension. Other features regarding _HD-D_ (hdd42_cw), number of sentences (nsentences), word frequencies (Freq_HAL_CW & COCA_fiction_Frequency_CW & COCA_Fiction_Bigram_Frequency), LSA-based indices (lsa_all_fwd & lsa_2-3_rwd), number of content words in text with frequency score (Brown_Freq_CW_Log), and word concreteness (Brysbaert_Concreteness_Combined_CW) were identified as predictive linguistic features for the writing quality of _Ideas_. As for _Organization_, analysis of the summary plot (see Figure 7b) revealed that features such as number of words (nwords) and _HD-D_ (hdd42_cw) displayed a predominantly positive distribution of SHAP values, implying that these features were associated with a  
Figure 7: SHAP values: visualization of the top 10 features for different constructs of writing quality. more favorable assessment of the 'Organization' dimension. On the other hand, features such as word frequencies of content words (Freq_HAL_CW) exhibited a distribution towards negative SHAP values, suggesting a negative association with the writing quality in terms of _Organization_. Scholars have indicated words that were more frequently used would be acquired easily and earlier by students [59; 64; 66]. Thus, more frequent uses tended to get lower scores in writing tasks. With Regard to _Style_, the results revealed that _HD-D_ (hdd42_cw) had the highest mean absolute SHAP value, indicating it was the most influential in the model's assessment of writing quality (see Figure 7c). Additionally, linguistic features including number of sentences (nsentences), number of words (nwords), lexical diversity indices (mtld_original_cw & mtld_ma_bi_aw & mtld_original_aw & root_ttr_fw), the similarity between two adjacent sentences (lsa_2_all_sent), number of positive logical connectives (positive_logical), and lexical density (lexical_density_types) were identified as predictive linguistic features for the overall writing quality of exposition. The results aligned with previous research, showing that the richness of vocabulary within a text was an important indicator of expository writing quality [66; 71; 74]. For _Conventions_ (see Figure 7d), the analysis revealed that the number of sentences (nsentences) and measure of textual lexical diversity (mtld_ma_bi_aw) were the features with a predominantly positive distribution of SHAP values. This suggested that these features were likely to correlate positively with writing quality. In contrast, the feature measuring the number of words in the text with a frequency score (Brown_Freq_AW_Log) displayed a distribution towards negative SHAP values, potentially indicating a negative relationship with writing quality. The findings corroborate earlier research, indicating that less proficient writers tend to employ a higher number of high-frequency words compared to their more proficient counterparts. This pattern suggests a reliance on more common vocabulary among writers with lower proficiency levels [15; 55]. The above analysis through SHAP vividly illustrates the multifaceted nature of linguistic features characterizing writing quality. Existing AES models, leveraging machine learning algorithms, are often criticized for their opaque, black-box nature, which hinges critically on the caliber of the training datasets. Integrating XAI methodologies, particularly SHAP, into these algorithms marks a pivotal advancement in demystifying the underlying mechanisms and providing a transparent analysis of linguistic features that characterize different constructs of writing quality.