**1. Introduction**

Content resources, interaction patterns, collaborative models, organizational planning and influencing factors related to learning processes constitute learning behaviors, which are also key elements to describe learning behaviors [1, 2]. The learning processes supported by online technology and data technology ensure the completeness and continuity of learning behavior data. Massive learning behaviors is an important part of education big data, which provides the possibility for the full development of learning analytics [3, 4]. Learning behavior data can be divided into two categories: horizontal format and vertical format from the perspective of data

structure and feature attributes. These two categories are inseparable about the components of learning behaviors, which are the atomic units to describe learning behaviors, such as url, forumng, questionnaire, etc. The horizontal format of learning behaviors is a vector set composed of multi-dimensional attributes, and the vertical format is a vector set composed of multi-level learners. From the perspective of horizontal format, the researches define learning behaviors as the collection of learners, appropriate learning analytics and tools are used to carry out data statistics and rule exploration. However, it is difficult to calculate and compare the influences of components of learning behaviors, which is not conducive to the construction of a new education mode, and it is relatively difficult to implement the calculation and comparison of the influences of learning behaviors more passive.

Learning behaviors represent continuous learning processes, and there are associated needs and execution results [5]. The analysis of learning behavior based on vertical format can provide more intuitive and accurate characteristics for the study of the groupness and individuality of the learning behavior components. However, the analysis process based on the vertical format is a complex problem with multiple factors. It is impossible to find a suitable decision making and prediction framework. Through sampling, the breadth and depth of data processing are limited, and it is difficult to achieve a feasible decision. Due to the shortcomings and gaps in technology and model, learning behaviors constitute data and potential relationships cannot be gotten fully mining and complete analysis. In terms of research methods and application practices of learning behaviors, there are still many problems to solve [6, 7].

In this chapter, vertical data is analyzed for an online learning behavior big data set. The vertical data analysis of learning behaviors is carried out from the data structure and characteristics. Based on Eclat framework, a probabilistic frequent itemset learning algorithm is designed, and its feasibility and reliability are demonstrated and compared. Within the effective performance indicators, the probabilistic frequent itemsets and association rules are calculated and mined from the learning behavior components, and the correlation is demonstrated. Then we explore the rules and characteristics of learning behaviors, and provide decision feedback and suggestions for the design improvement and relationship of learning behaviors.

#### **2. Related work**

Mining probabilistic frequent itemsets is a branch of data analytics. There are explicit or implicit association data, which is the key basis for prediction, decision making and recommendation of other learning behavior components. On the current big data platform, the decision algorithm and recommendation algorithm based on frequent itemsets mining are used to track data. However, due to the particularity and complexity of learning behaviors, as well as the autonomy and randomness of learning processes, there is no general technical means to ensure the integrity and sufficiency to implement the analysis and calculation with the goal of decision making and prediction. In this regard, it is necessary to participate in benign learning behavior component construction and recommendation. The research on frequent itemsets has shown an urgent technical demand in the field of education big data. There have been relevant results to demonstrate the urgency and reality of empirical methods and technical means.

The research on probabilistic frequent itemsets of learning behavior components, after combing the relevant theoretical and application results, mainly focuses on the data statistics and association rules of horizontal format, which is reflected in the following aspects:

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