Research and Design of Auxiliary Teaching System for College Students’ Civics and Political Science Courses in the Context of Deep Learning

Abstract

At present, the auxiliary teaching system for Civics and Politics courses has problems such as low accuracy of knowledge state prediction and insignificant effect of personalized learning, which affects the actual learning effect. In this paper, we analyze the demand for the teaching of college students’ Civics and Politics based on the background of deep learning, and discuss the overall design of the system. Based on the open-source online teaching system CAT-SOOP, a set of augmented learning algorithm-based Civics course assisted teaching system is designed and implemented, which is based on the student practice data, training student knowledge tracking model and augmented learning recommendation engine for assisting the personalized recommendation of student’s Civics exercises. The results show that compared with the random recommendation method, the relevance of the recommended exercises of this system is improved from 0.03 to 0.238, the reward value is more stable, and the maximum value is improved by 0.2. The Civics course assisted teaching system designed in this paper for college students achieves the expected goals and meets the diversified needs of the audience of intelligent Civics education.