Comparison of Classification Performances of MARS and BRT Data Methods: AB?DE-2016 Case

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Şevgin H., ÖNEN E.

EGITIM VE BILIM-EDUCATION AND SCIENCE, vol.47, no.211, pp.195-222, 2022 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 47 Issue: 211
  • Publication Date: 2022
  • Doi Number: 10.15390/eb.2022.10575
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, EBSCO Education Source, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.195-222
  • Keywords: Data mining, Multivariate adaptive regression&nbsp, splines, Boosted regression trees, AB?DE, Science achievement, SELF-EFFICACY BELIEFS, SCIENCE ACHIEVEMENT, SCHOOL CLIMATE, BULLY/VICTIM PROBLEMS, STUDENTS, PEER, INVOLVEMENT, PREDICTORS
  • Gazi University Affiliated: Yes


This research examined the relationships between student, teacher, school and instructional qualifications and 8th grade students' science achievement, based on the conceptual framework created by Nilsen and Gustafsson (2016), using data mining methods MARS and BRT. Research data (n=10407 students, n=941 teachers and n=865 school administrators) were obtained from the AB??DE study conducted at the national level by the Ministry of National Education in 2016. MARS and BRT analyzes were performed in the SPM 8.2 program. The science achievement classification performances of these methods were compared by considering the correct classification rate, sensitivity and specificity rates, F1 statistical value and the area under the ROC curve. It was found that the BRT method was more successful than the MARS method in terms of all these criteria, and the most important predictors of science achievement were similar compared to these two methods. The results revealed that the most important predictors of science success are the student's perception of science self-efficacy, the father's occupation, the family's monthly income, the instructional activities of the teacher, the teacher's experience and preparation for the lesson, and the school administrators' perception of school climate. It is thought that the reason why BRT outperforms the MARS method in terms of the criteria considered in this study is that BRT learns from errors with the additive combination of various regression trees and provides a stronger classification performance by minimizing the errors that may occur in classification. This study revealed the benefits of using these two data mining methods in the field of Educational Sciences and discussed the contribution of the related methods in this field.