Thinking on Student Evaluation Strategies Based on College Physics Cognitive Graph

The reform of the college entrance examination is carried out in batches. The differences in the knowledge level and structure of college freshmen are increasing. Existing evaluation schemes pay less attention to the knowledge structure that has an important impact on students’ cognition, making it difficult to accurately identify students with learning difficulties. In this regard, the paper categorizes students’ types in the transition from middle school to college education based on their knowledge mastery into four types: well-balanced excellent transition, unbalanced excellent transition, average balanced transition, and average unbalanced transition. The one-way and two-way Apriori algorithm are used to mine the physics learning data of undergraduate freshmen and generate association rules. Then, an academic knowledge graph based on textbooks is used to filter the association rules, forming personalized cognitive maps for students of different types. The results show that the knowledge structure of students in the process of transition between college and middle school is basically positively correlated with students’ performance, and there are obvious differences in the knowledge structure of the four types of students. Finally, based on the research results, some suggestions are provided for the accurate evaluation strategies of students in the context of the new college entrance examination reform.