This essay focuses on behavior in the e-learning system. The aim of this article is to investigate which recorded elements of student behavior
The purpose of decision trees is to identify specific object classes. Decision trees use different object attributes to classify different object subsets and do not use just one attribute or a fixed set of attributes.7 The attractiveness of decision trees is in their easiness for understandability and interpretability. The aim of this article is to investigate which recorded elements of student behavior in the e-learning system could contribute to successful passing of exams in the observed ecourse. The research questions this article is trying to answer are as follows: 1. Which student information can be extracted from event logs of an
2. Which variable values have a significant influence on grouping students with regard to their behavior in the e-learning system? The motivation for writing this article comes from finding a course that is interesting to analyze due to its variety of student activities base on which advance data mining techniques can be apply to improve content management in that course. The quality of e-course execution at higher education institutions in Croatia reflects the quality of teaching according to which higher education institutions are rank. In the literature review, an analysis of the existing literature is conduct. In this chapter, educational data mining, application
technique are research. Further on, research methodology of this article is present with the aim of introduction on research data and research technique. Methodology is follow by a description of the results obtain by cluster analysis and decision tree technique. Article ends with final discussion remarks on perceive knowledge and future work. Literature review Logs could contain a wide range of information about process executions.8 Data mining shares some characteristics with automatic process discovery techniques, and in data mining, “meaningful information is extracted from finegranular data, so that these techniques of automatic process discovery are subsumed to the research area of process mining.
is the process of extracting useful information and knowledge from a large set of data warehouses.he level of education are as follows: Creating data sources of predictive variables. Identification of different characteristics or factors that influence student learning performance during academic life. Construction of a predictive model using classification data mining techniques based on predictive variables. ; 1.Firstly, Data collection. Refers to collecting all available student information. Users create data files starting with e-learning databases.9 2.secondly, Data preprocessing. At this stage, a data set is prepare for the application of data mining techniques. To successfully complete this stage, data
Further, be creative
Lastly, be innovative
Lastly, be sober