Interactive intelligent teaching and automatic composition scoring system based on linear regression machine learning algorithm

Abstract

Intelligent education is an intelligent education platform that integrates correct education concept and Internet of things, big data, cloud computing and other technologies. This paper hopes to use the construction of the composition scoring model to further construct a computer scoring system for college English translation, which can give students a translation score and give feedback evaluation based on the quality of the translation. In this paper, according to the knowledge of the existing automatic scoring system at home and abroad, the feature selection method (TF-IDF, IG, CHI) is discussed and analyzed. Moreover, this paper studies the impact of our composition automatic scoring from the perspective of linguistics. In addition, this paper uses the multiple regression method to evaluate the final score. The features considered in this paper mainly include simple linguistic features and complex linguistic features. Finally, performance analysis of the algorithm model is performed by setting up a control experiment. The research results show that the proposed algorithm model has certain effects. The future trend is to form adult auxiliary machines through various human-computer interaction technologies, which will reshape future learning and education and form a new teaching form.