Research on Online Learning Behavior Analysis Model in Big Data Environment
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Wang Peng 1 *
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1 China Business Executives Academy, Dalian (CBEAD), CHINA
* Corresponding Author

Abstract

In this study, on the basis of summarizing the current situation of online learning behavior and related theoretical research. Based on the analysis of related research results, considering the existing problems, the main contents of this paper include the following aspects: (1) Define the connotation of online learning behavior, and introduce the theory of artificial intelligence into the classification of online learning behavior from structural dimension, functional dimension and mode dimension; (2) According to the overall architecture of the analysis model, the analysis model is constructed from left to right and top to down under the big data environment. The online learning behavior data model is constructed from the multi-dimensional and multi-level perspective to determine the source, method and process of data collection. After that, designs the horizontal and longitudinal processes of the online learning behavior analysis model. On this basis, using the big data processing technology on the online learning behavior analysis model in all aspects of the specific algorithms involved in the implementation. (4) We chose the online learning platform of China Business Executives Academy, Dalian to do empirical analysis. In-depth study from the following three aspects: the learning behavior clustering analysis based on K-means algorithm, the individualized course recommendation analysis based on Page Rank algorithm and the correlation analysis of learning effects.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.12973/eurasia.2017.01021a

EURASIA J Math Sci Tech Ed, 2017 - Volume 13 Issue 8, pp. 5675-5684

Publication date: 22 Aug 2017

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Article Downloads: 5197

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