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MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to mine and predict the value and laws of MOOCs is a difficult problem in learning analytics applied to MOOCs. This study integrates the context information of the learning process of MOOCs, designs a deep learning model based on multi-entity knowledge graph, in order to predict and track the interest propagation of learning behaviors, and realizes the learning trend guidance mechanism supported by multi features and complex relationships. Through sufficient experiments, the effectiveness and reliability of this model are verified, and based on the analysis results, the best path and implementable scheme for the interest propagation of learning behaviors are constructed. The whole research might provide more references for the study of learning behaviors described by multi-entity knowledge graph of MOOCs.

The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs.

Interactive learning is a two-way learning method of learners independently by using computer and network technology. In the interactive relationships, interactive learning plays a role for learners to achieve the learning purpose, interactive learning has become an important effect of online learning, but it also has many problems that need to be improved. In particular, how to apply the massive data to achieve data-driven early warning needs further research. This study firstly mines the temporal series of learning behavior features, the corresponding data is collected from one online learning platform of The UK Open University, the massive data obtained from the platform have been desensitized, disclosed and shared, which might ensure the comparison and verification of research results. Secondly, we design a early warning mechanism based on temporal memory enhancement model. Through a large number of data training and testing, this method is useful to the analysis of learning behavior features, and has strong effectiveness and reliability. Thirdly, the decision and intervention mechanism are mined and predicted. The whole work is of great significance to the early warning mechanism of interactive learning process, which has strong theoretical value and practical significance.

This study analyzed the association between parental conflict and adolescents' problematic Internet use (PIU), especially the mediating role of adolescent emotional dysregulation and the moderating role of adolescents' effortful control. About 870 middle school students participated in this survey, reporting on their perceived parental conflict, their own emotional dysregulation, effortful control, and PIU. The results demonstrated that the association between parental conflict and adolescent PIU was completely mediated by adolescent emotional dysregulation. Adolescent effortful control was found to moderate the indirect relation of parental conflict to adolescent PIU, with parental conflict more strongly associated with PIU for adolescents with lower levels of effortful control. Our findings add to the extant insights on how parental conflict could contribute to adolescent PIU. [ABSTRACT FROM AUTHOR]