This essay focuses on cluster analysis and decision tree.. The cluster analysis was performed by organizing collections of patterns into groups
Abstract Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia.
Data use for the analysis are event logs download from an e-learning environment of a real e-course. Data mining techniques apply for the research are cluster analysis and decision tree. The cluster analysis was perform by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.
Keywords Educational data mining, cluster analysis, decision trees, case study, log file Date received: 30 September 2019; accepted: 18 January 2020 Introduction Data mining. This is a widely spread approach for analyzing large data repositories. This is to extract necessary or useful information. The goal of data mining application is to extract hidden data patterns and to detect relationships. This is between parameters in a vast amount of data. The exploration of data in education using data mining techniques.
It is commonly know as educational data mining.1 Different educational data are store in large databases. This is especially true for online programs, for the support of teaching processes and in which student learning behaviors can be record and store. Tgineering Business Management Volume 12: 1–9 ª The Author(s) 2020 DOI: 10.1177/1847979020908675 journals.sagep.
Firstly, be sober
Thirdly, be straight
Further, be keen
Further, be fast
Lastly, be smart