Artificial Intelligence and Learning Effects of Reading Classics

This study analyzes the machine learning and deep learning models that were used to forecast the satisfaction effect of classics reading classes. The following were the main findings of the comparison of their predictive abilities. First, the traditional regression model is somewhat low in coefficient of determinant. Second, the decision tree models predicts the satisfaction effect of classics reading classes better than the traditional regression model. Third, when we predict the learning effects of classics reading lessons, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, when we predict the learning effects of classics reading lessons, the deep neural network models also show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the satisfaction of classics reading classes more accurately, we need to adopt the machine learning and deep learning models to predict the satisfaction of classics reading classes using the learning variables.