**7.4 Learning behavior needs the adaptive support of specific algorithm and data structure**

The generation of learning behaviors is a multi-dimensional process. The research strength of these data determines the cognitive strength of learning behaviors. There are different perspectives on the composition of learning behaviors, which determines different research methods. How to carry out relatively sufficient modeling description and business processing of learning behaviors presents challenges to learning analytics. Some existing software tools and analysis methods can not guarantee the appropriate quantification, standardization and initialization, the analysis process and experimental conclusion may not be thorough and comprehensive. Compared with the statistics and test of learning behaviors carried out by sampling, the effective and comprehensive analysis of learning behaviors is more convincing.

Therefore, the empirical analysis of learning behaviors should be the comprehensive application process of data-driven technologies and methods. Combined with the data characteristics, the technical requirements are demonstrated, and the structures and algorithms suitable for data attributes and process characteristics are designed. This aspect has huge research space and prospect in the field of education big data, which poses challenges for researchers. Learning analytics of educational big data is essentially data analysis, and it is a comprehensive application of computer science and technology, statistics, engineering, etc., and the design and development of general tools in this respect still need time [28]. For a specific data set, it is feasible and more realistic to design suitable data structures and algorithms for decision making.

*Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on… DOI: http://dx.doi.org/10.5772/intechopen.97219*
