Research on Online Learning Behavior Analysis Model in Big Data Environment
Wang Peng 1  
 
 
More details
Hide details
1
China Business Executives Academy, Dalian (CBEAD), CHINA
CORRESPONDING AUTHOR
Wang Peng   

Department of Executive Education, China Business Executives Academy, Dalian, China. Address to: No.777, HongLing Rd., High-tech Zone, Dalian City 116086, China. Tel: +8618941119125
Online publish date: 2017-08-22
Publish date: 2017-08-22
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):5675–5684
KEYWORDS
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.
 
REFERENCES (9)
1.
Basurto, X. (2013). Linking multi-level governance to local common-pool resource theory using fuzzy-set qualitative comparative analysis: Insights from twenty years of biodiversity conservation in Costa Rica. Global Environmental Change, 23(3), 573–587. doi:10.1016/j.gloenvcha.2013.02.011.
 
2.
Biasutti, M. (2017). A comparative analysis of forums and wikis as tools for online collaborative learning. Computers & Education, 12(7), 158–171. doi:10.1016/j.compedu.2017.04.006.
 
3.
Cormas, P. C. (2016). Sequenced Integration and the Identification of a Problem-Solving Approach through a Learning Process. Eurasia Journal of Mathematics, Science & Technology Education, 8(12), 2557–2574. doi:10.12973/eurasia.2016.1258a.
 
4.
El-Masri, M. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). ETR&D-Educational Technology Research and Development, 12(65), 743–763. doi:10.1007/s11423-016-9508-8.
 
5.
Lai, H. J. (2017). Examining civil servants’ decisions to use Web 2.0 tools for learning, based on the decomposed theory of planned behavior. Interactive Learning Environments, 11(25), 295–305. doi:10.1080/10494820.2015.1121879.
 
6.
Liu, S. H. (2017). Relationship between the factors influencing online help-seeking and self-regulated learning among Taiwanese preservice teachers. Computers in Human Behavior, 60(7), 38–45. doi:10.1016/j.chb.2017.02.034.
 
7.
Liao, Y., & Lin, W. H. (2016). Effects of matching multiple memory strategies with computer-assisted instruction on students’ statistics learning achievement. Eurasia Journal of Mathematics, Science & Technology Education, 9(12), 2921–2931. doi:10.12973/eurasia.2016.02313a.
 
8.
Staffileno, B. A., & Zschunke, J. (2017). The Feasibility of Using Facebook, Craigslist, and Other Online Strategies to Recruit Young African American Women for a Web-Based Healthy Lifestyle Behavior Change Intervention. Journal of Cardiovascular Nursing, 7(32), 365–371. doi:10.1097/JCN.0000000000000360.
 
9.
Zhang, S., & Liu, Q. T. (2017). A study of peer coaching in teachers’ online professional learning communities. Universal Access in the Information Society, 9(16), 337–347. doi:10.1007/s10209-016-0461-4.
 
eISSN:1305-8223
ISSN:1305-8215