# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 3 Results
### Scoring Model (RQ3)
#### 3.3.1 Traditional Machine Learning Approaches  
Traditional machine learning methods typically rely on carefully designing, extracting, and selecting features highly relevant to essay scoring. To construct scoring models, these methods utilize conventional regression, classification techniques, or feature similarity. (1) Traditional classification and regression models  
Figure 5: Usage of features. Regression models can predict scores in continuous numerical form. The most commonly used scoring model in AEE is the multiple linear regression model that performs stepwise linear regression analysis on various extracted features to obtain the optimal combination of weighted features (i.e., the most important factors affecting scoring) and establishes a scoring model. Regression models help predict continuous numerical scores. The most commonly used scoring model is the ACEE's multiple linear regression (MLR) [18; 19; 33; 65]. These models employ stepwise linear regression analysis on extracted features to obtain the optimal combination of weighted features, representing the most influential factors affecting scoring. The scoring model constructed using linear regression demonstrates a high correlation with scores assigned by human raters and possesses strong interpretability. Support vector regression (SVR) [41; 57; 59] is an extension of SVM, where the hyperplane decision boundaries calculated by SVM are utilized as the regression model to predict essay scores. It is commonly used in AEE for scoring tasks with small corpora. Besides these models, Bayesian regression [36], Bayesian linear ridge regression [62], and gradient boosting decision tree (GBDT) regression models help predict continuous numerical scores. The most commonly used scoring model is the ACEE's multiple linear regression (MLR) [18; 19; 33; 65]. These models employ stepwise linear regression analysis on extracted features to obtain the optimal combination of weighted features, representing the most influential factors affecting scoring. The scoring model constructed using linear regression demonstrates a high correlation with scores assigned by human raters and possesses strong interpretability. Support vector regression (SVR) [41; 57; 59] is an extension of SVM, where the hyperplane decision boundaries calculated by SVM are utilized as the regression model to predict essay scores. SVR-based methods are commonly used in AEE for scoring tasks with small corpora. Besides these models, Bayesian regression [36], Bayesian linear ridge regression [62], and gradient boosting decision tree (GBDT) [45] have also shown promising performance in ACEE scoring tasks. For AEE research involving essay scoring as categorical data, classification methods are employed for building scoring models. Ordered logistic regression [35], SVM [38; 40; 44; 66], decision trees [20], and random forests [43] are among the methods used to construct classification models for essay scoring. Due to its nonlinear characters and high-dimensional features, SVM is particularly suitable for constructing scoring models that offer high accuracy without requiring large-scale corpora, so it is a preferred method with which researchers build classification scoring models. (2) Feature similarity model  
Some studies build scoring models by evaluating the similarity of specific features between the target and standard essays of different proficiency levels to assign scores. Latent Semantic Analysis (LSA) is a typical model that scores essays based on semantic similarity by computing the similarity in the latent semantic space between the target essays and standard essays [21]. Other features beyond latent semantics have also been utilized in constructing similarity-based scoring models. [PERSON], [PERSON] et al.