Statistical evaluation of the achievements of professional students by combination of the random forest algorithm and the ANFIS method

Marija Mojsilović; Radoje Cvejić; Selver Pepić; Darjan Karabašević; Muzafer Saračević; Dragiša Stanujkić

doi:10.1016/j.heliyon.2023.e21768

Statistical evaluation of the achievements of professional students by combination of the random forest algorithm and the ANFIS method

Heliyon. 2023 Oct 31;9(11):e21768. doi: 10.1016/j.heliyon.2023.e21768. eCollection 2023 Nov.

Authors

Marija Mojsilović¹, Radoje Cvejić², Selver Pepić¹, Darjan Karabašević³, Muzafer Saračević⁴, Dragiša Stanujkić⁵

Affiliations

¹ Academy of Professional Studies Sumadija Department in Trstenik, Trstenik, Serbia.
² Faculty of Information Technology and Engineering FITI, University Union Nikola Tesla, Belgrade, Serbia.
³ Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Belgrade, Serbia, Belgrade, Serbia.
⁴ Department of Computer Sciences, University of Novi Pazar, Novi Pazar, Serbia.
⁵ Technical Faculty in Bor, University of Belgrade, Bor, Serbia.

Abstract

This research is of great importance because it applies artificial intelligence methods, more specifically the Random Forest algorithm and the Anfis method to research the key factors that influence the success of students in vocational schools. Identifying these influencing factors is not only useful for improving curriculum and practice but also provides valuable guidance to help students master the material more effectively. The main goal of this research is to penetrate deeply into the core of the factors that influence the success of students in vocational schools, using two different methods. Each of the factors represented as input is mutually independent and does not affect each other, but each of them affects the output variable. The parameters considered as input variables are prior programming knowledge and pretest requirements. Then, by finding one factor that has the greatest influence, the factor of pre-exam obligation was investigated in more detail, using the Anfis method, which was broken down into several input parameters. These results emphasize the importance of the combination of the Random Forest algorithm and the ANFIS method in the statistical evaluation and assessment of student achievement in vocational schools. This study provides useful guidelines for improving education and practice in vocational schools to optimize educational outcomes.

Keywords: Anfis; Artifical intelligence; Computational intelligence; Education; Random forest.