## 5 Experimental Results
The Lexile Writing Analyzer makes use of a web-based application called the Lexile Analyzer to calculate the Lexile level of polished, edited, and complete writing. There is now an automated scoring market in the United States, and these commercial systems have cornered more than 97% of it [71,72]. There is no feedback given by these systems; they simply provide grades. The results shown in Table 6 confirm that the proposed model is better than the systems compared with it in terms of quadratic weighted kappa and average accuracy. As not all the systems are offered for public testing, their results were acquired from the work presented in [73] and from the Kaggle website. The evaluated systems are organized in descending order of the average Kappa value, and commercial systems are ranked according to average accuracy. The proposed model was better than PEG by 3.86%, E-rater by 5.55%, and Lexical by 2%. These systems use statistical methods to extract the lemmas (tokens after stemming) into a vector, and then they begin dealing with these lemmas as numbers only, which means they do not care about the word order or the coherence and consistency of the essay. Meanwhile, in the suggested approach, extra features, such as spatial analysis and spatial autocorrelation, are used to assess the essay, improving accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **DS\({}_{1}\)** & **DS\({}_{2}\)** & **DS\({}_{3}\)** & **DS\({}_{4}\)** & **DS\({}_{5}\)** & **DS\({}_{6}\)** & **DS\({}_{7}\)** & **DS\({}_{8}\)** \\ \hline AGE System & 0.91 & 0.75 & 0.82 & 0.84 & 0.89 & 0.73 & 0.88 & 0.79 \\ AGE+ system & 0.93 & 0.80 & 0.84 & 0.85 & 0.88 & 0.77 & 0.89 & 0.80 \\ Neutrosophic ontology model & 0.95 & 0.83 & 0.88 & 0.86 & 0.91 & 0.84 & 0.91 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AEG, AGE+ and the Suggested Model for different data set in terms of _Kp_. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **DS\({}_{1}\)** & **DS\({}_{2}\)** & **DS\({}_{3}\)** & **DS\({}_{4}\)** & **DS\({}_{5}\)** & **DS\({}_{6}\)** & **DS\({}_{7}\)** & **DS\({}_{8}\)** \\ \hline AGE System & 0.88 & 0.72 & 0.78 & 0.75 & 0.77 & 0.74 & 0.86 & 0.77 \\ AGE+ system & 0.90 & 0.79 & 0.82 & 0.81 & 0.79 & 0.76 & 0.87 & 0.79 \\ Neutrosophic ontology model & 0.94 & 0.82 & 0.85 & 0.83 & 0.90 & 0.78 & 0.90 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of AEG, AGE+ and the Suggested Model for different data set in terms of _Kp_. In experiment 4 (Comparative study with machine learning models), the suggested model is compared to several machine learning models in the field of essay scoring. These models are the recurrent neural network [74], which predicts a score for a given essay, defined as a sequence of words, by following multi-layered neural networks; similarity measures [75,76], in which student answers are compared with model answers and then scores are allocated; and word order graph [77], which uses word-order based graph representation for text. The word order graph is able to capture the order of the tokens and their lexicon-semantic. Then, it utilizes the matching technique for identifying the degree of relatedness across tokens and phrases. The suggested model was superior for evaluating essays. Quadratic weighted kappa was used to compare the results in the three experiments. Table 7 indicates that the proposed model results were significantly better than the neural network-based model (CO-Attention) by 10%. Table 8 shows that the proposed model exceeded the model that utilizes the Jaccard coefficient by 11%, the model that utilizes the dice coefficient by 15%, and the model that utilizes the cosine coefficient by 17%. The results in Table 9 show that the proposed model is better than the algorithm that uses word graphs by 5.5%.