A study of constructed paths based on big data analysis facing the framework of modern and contemporary literature education

Using big data analysis technology to build a professional knowledge point network is one of the hotspots of current teaching research. This paper describes the advantages of mining students’ knowledge point learning behavior data in the process of literature learning. By identifying the connotations of knowledge point models, knowledge point judgment methods, and association rule mining methods, the overall process of association rule mining for literature knowledge points is constructed. The 208 knowledge points from the next book of History of Modern Chinese Literature 1915-2022 (Fourth Edition) have been extracted for association rule mining and parameter analysis among the knowledge points in the textbook. Based on frequent patterns and 6000 test questions in the examination system of History of Modern Chinese Literature, association rules between simultaneous right and wrong answers are mined, and associations between related knowledge points of test questions are analyzed. Through the calculation, it is obtained that a knowledge point is associated with about 5 knowledge points on average, and most of the knowledge points in the knowledge network of the textbook can be directly associated with 2 or more knowledge points, and there is more than one way of association. Knowing that the mastery of the knowledge points in the pre-test questions will affect the accuracy of the answers to the post-test questions, teachers can adjust their teaching planning according to the mastery of students’ knowledge points. Constructing a knowledge base network on modern and contemporary literature can effectively improve students’ learning efficiency and facilitate teachers in providing personalized teaching.