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## Data Mining for the e-Learning Risk Management

#### Oksana GUSHCHINA [1] , Andrew OCHEPOVSKY [2]

##### 6 14

The article shows the role of data mining methods at the stages of the e-learning risk management for the various participants. The article proves the e-learning system fundamentally contains heterogeneous information, for its processing it is not enough to use the methods of mathematical analysis but it is necessary to apply the new educational methods of data mining. It determines the basic types of e-learning risks for the elimination or minimization of which proactive manners are applied that aimed at testing and planning other actions. The article gives the rationales for the use of various data mining methods at the different stages of the risk management: 1) for quality authentication of the risks the classification based on using the method called “decision tree” is applied; 2) for making the analysis of risks the brainstorm method for mind map creation is used. The mind map displays the e-learning risk groups that are then rated on the prioritization criteria with the help of expert evaluation method; 3) for the assessment of probability of risk impact on organization of e-learning the expert evaluation method, cluster analysis and bow-tie analysis are used. The article shows that the data mining methods are able to not only classify educational risks but also identify the causes and anticipate possible impacts on the final outcome. Having a great deal of information obtained through the process of the e-learning risk management and using it in data mining, it is possible to determine the reasons and the taken actions depend more the minimization of risks and strengthen the effectiveness of the e-learning.

e-Learning, educational risk, risk management, data mining
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Primary Language en Social Articles Orcid: 0000-0003-2381-8537Author: Oksana GUSHCHINA (Primary Author) Orcid: 0000-0002-5270-5390Author: Andrew OCHEPOVSKY Publication Date: July 1, 2019
 Bibtex @research article { tojde601941, journal = {Turkish Online Journal of Distance Education}, issn = {1302-6488}, address = {Anadolu University}, year = {2019}, volume = {20}, pages = {181 - 196}, doi = {10.17718/tojde.601941}, title = {Data Mining for the e-Learning Risk Management}, key = {cite}, author = {GUSHCHINA, Oksana and OCHEPOVSKY, Andrew} } APA GUSHCHINA, O , OCHEPOVSKY, A . (2019). Data Mining for the e-Learning Risk Management. Turkish Online Journal of Distance Education, 20 (3), 181-196. DOI: 10.17718/tojde.601941 MLA GUSHCHINA, O , OCHEPOVSKY, A . "Data Mining for the e-Learning Risk Management". Turkish Online Journal of Distance Education 20 (2019): 181-196 Chicago GUSHCHINA, O , OCHEPOVSKY, A . "Data Mining for the e-Learning Risk Management". Turkish Online Journal of Distance Education 20 (2019): 181-196 RIS TY - JOUR T1 - Data Mining for the e-Learning Risk Management AU - Oksana GUSHCHINA , Andrew OCHEPOVSKY Y1 - 2019 PY - 2019 N1 - doi: 10.17718/tojde.601941 DO - 10.17718/tojde.601941 T2 - Turkish Online Journal of Distance Education JF - Journal JO - JOR SP - 181 EP - 196 VL - 20 IS - 3 SN - 1302-6488- M3 - doi: 10.17718/tojde.601941 UR - https://doi.org/10.17718/tojde.601941 Y2 - 2019 ER - EndNote %0 Turkish Online Journal of Distance Education Data Mining for the e-Learning Risk Management %A Oksana GUSHCHINA , Andrew OCHEPOVSKY %T Data Mining for the e-Learning Risk Management %D 2019 %J Turkish Online Journal of Distance Education %P 1302-6488- %V 20 %N 3 %R doi: 10.17718/tojde.601941 %U 10.17718/tojde.601941 ISNAD GUSHCHINA, Oksana , OCHEPOVSKY, Andrew . "Data Mining for the e-Learning Risk Management". Turkish Online Journal of Distance Education 20 / 3 (July 2019): 181-196. https://doi.org/10.17718/tojde.601941 AMA GUSHCHINA O , OCHEPOVSKY A . Data Mining for the e-Learning Risk Management. Turkish Online Journal of Distance Education. 2019; 20(3): 181-196. Vancouver GUSHCHINA O , OCHEPOVSKY A . Data Mining for the e-Learning Risk Management. Turkish Online Journal of Distance Education. 2019; 20(3): 196-181.