**7.3 The frequent itemsets of learning behavior have the characteristics of explicit and implicit association**

There are differences in interaction mode of learning behavior components in different platforms, but the demands of serving learning behaviors are the same, that is to realize the continuity of learning behaviors and achieve the learning effects through the interaction of components. Through the mining of probabilistic frequent itemsets and the analysis of association rules, the components of frequent itemsets have explicit association features, and different frequent itemsets may also have implicit association features. It has a strong recommendation value for the prediction and feedback of latent learning behaviors. The key topological relationships of learning behaviors are shown in **Figure 11**, that can provide references for the follow-up learning processes of similar or the same courses, and expand learning methods.

Therefore, the construction of learning behaviors should not only consider the learning content and teaching objectives, but also refer to the historical effective learning behaviors, and also need to carry out effective learning process reform and learning strategy change based on data analysis, gradually promote learners to develop effective learning habits and methods, and construct new learning behavior components. According to the learning situation, stage learning feedback, potential behavior recommendation and implicit interest mining are achieved in order to improve the learning quality.
