## 4 Discussion  
The results of this study show that the similarity score based on the embedding of sentences is an effective way of quantifying reading behavior. The similarity scores between the stimulus text and the responses were lower for poor comprehenders compared to normal students. This is consistent with previous research, indicating that poor comprehenders struggle with understanding the meaning of given sentences and often digress into unrelated topics [3]. It is believed that such behaviors are associated with an attention deficit or inefficient working memory [1; 3]. Our method provides quantitative evidence to support this theory. The main difference between the two embedding models is the use of context in the text and response. TF-IDF is based solely on the frequency of words and is insensitive to their order or relationship to neighboring words. In contrast, the SBERT model is trained to take contextual information into account by considering words around a target word. This difference resulted in qualitatively different sentence embeddings and higher classification accuracies of the SBERT model than those of TF-IDF. Even with simple linear classifiers, the embedding models produced qualitatively different F1 scores. The F1 scores of the logistic regression classifiers were similar for different embedding models. The F1 score of LDA based on TF-IDF was lower than that of the logistic regression classifier based on TF-IDF. In contrast, the F1 score of LDA based on SBERT was higher than that of the logistic regression classifier based on TF-IDF, resulting in a significantly higher F1 than the baseline classifier. This difference was due to the different assumptions made about the input to the classifiers. LDA assumes that the input is normally distributed. As shown in Figure 2, the distribution of similarity scores based on TF-IDF is bimodal, which does not fit the LDA assumption. In contrast, the distribution of similarity scores based on SBERT is unimodal, which is well modeled by LDA. Similarly, a comparison of LDA and QDA provides the following insights. Both LDA and QDA assume normality of the input distribution. The only difference is that LDA assumes that the same covariance matrix is shared between the classes, whereas QDA allows each class to have its own covariance matrix, allowing more flexibility in capturing the different distributions of the classes with a larger number of parameters. For both embedding models, the F1 values of the QDA are lower than those of the LDA. Thus, the lower accuracy of the more flexible classifier indicates overfitting. The F1 values of the Naive Bayes classifiers were slightly higher than those of logistic regression, but not significantly higher than those of the baseline classifier. This is because the feature dimension (10), which corresponds to the number of sentences in the stimulus text, is relatively low in this study. Thus, scaling the classifier for higher dimensional features at the cost of ignoring the correlation between dimensions provides little gain. Compared to the Naive Bayes classifiers, SVM and random forest classifiers produced higher classification accuracies. This suggests that the reader's response to an input text changes as the participant reads through the sentences in the text. Considering this non-stationary behavior allowed the SVM and random forest classifiers to achieve higher classification accuracies. For both embedding models, the F1 scores of the SVM classifiers were significantly higher than those of the baseline classifier. This is consistent with the previous finding that SVM works well, especially when the number of samples is not enough. The balance between the classification error and sensitivity to outliers can be controlled by the hyper parameter C. With the optimal choice of hyperparameter, SVMs achieved high F1 scores of 0.65 (TF-IDF) and 0.68 (SBERT), with corresponding recall scores of 0.5 (TF-IDF) and 0.46 (SBERT).