Open AccessAnalysis of the Performance of the Main Algorithms for Educational Data Mining: A Review
Author(s) -
Mustafa Abdalrassual Jassim
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1090/1/012084
Subject(s) - computer science , educational data mining , data mining , process (computing) , context (archaeology) , algorithm , set (abstract data type) , data set , statistical classification , machine learning , data science , data stream mining , knowledge extraction , artificial intelligence , paleontology , biology , programming language , operating system
Data mining is the process of extracting useful and valuable information from vast amounts of data. Algorithms and various tools to use are some of the most popular data mining applications to estimate future events based on past experiences. Many researchers use techniques and tools to extract useful data to address and solve higher education problems in this context. EDM analyzes educational data using methods and algorithms to develop and apply DM data extraction to the information stored in academic data repositories. As a result, it provides essential knowledge of the teaching and learning process for successful and effective educational planning. This paper focuses on comparing the algorithms’ performance and applied to the same educational data set. Educational Data Mining (EDM) uses these algorithms to explore patterns, educational statistics, and predictions in the data. Thus, statistics are generated based on all classification algorithms. A comparison of all eight classifiers was made to predict results and find the best performance classification algorithm accurately. This paper aims to use a literary survey to determine the most appropriate algorithm according to EDM’s needs.