Excellent Teaching Patterns Mining Based on Formal Concept Analysis

Formal Concept Analysis(FCA) is a powerful tool for data analysis and rule extraction. It can mine the hidden concepts in the data and the hierarchical relationships between concepts by establishing the binary relationships between the object set and the attribute set. The analysis of students' and teachers' data are conducted with FCA in this paper. By creating the formal background and concept lattice of students' and teachers' data sets, it analyzes in detail the relationship between students' answer and their mastery of knowledge and skills, as well as the relationship between excellent teachers' teaching behaviors and teaching effects, and combines constructivism theory, scaffolding teaching theory and the theory of the zone of proximal development to mine high-quality teaching models. Then, with the method based on statistics, the distribution of students' answers in the test and the distribution of teachers' teaching behavior are analyzed. Finally, a case study on teaching mode mining was conducted based on existing benchmark datasets, and the implementation results showed that the proposed method in this paper can effectively mine high-quality teaching modes at a deeper level, providing reference for further enhancing students' self-learning ability and improving teacher teaching behavior.