AI Summary & Key Takeaways
AI InsightHaving 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.
📌 3 Key Takeaways
Abstract
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.