Education Data Mining on PISA 2015 Best Ranked Countries: What Makes the Students go Well

Santos, Rachel de Salles Freitas dos; Paulista, Cássio Rangel; Hora, Henrique Rego Monteiro da

doi:10.1007/s10758-021-09572-9

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…This study differs from other studies in the literature on PISA in terms of the EDM methods and variables used [4][5][6][7]. Random Forest [4,7], Discriminant Analysis [8], Decision Tree [5,7,9], Logistic Regression [10], Naïve Bayes [11,12] studies are encountered in the literature.…”

Section: Introductionmentioning

confidence: 73%

“…The 'Highest Occupation Status of Parents' variable also has a positive effect on student success. Highly educated parents' children are more successful than other students [6]. In addition, 'Students' Expected Occupation Status' also has a positive effect on success.…”

Section: Discussion Conclusion and Recommendationsmentioning

confidence: 96%

“…When the results are examined, it is seen that the 'Home Possessions' variable has a positive effect on mathematics achievement [6,20]. The 'Highest Occupation Status of Parents' variable also has a positive effect on student success.…”

Section: Discussion Conclusion and Recommendationsmentioning

confidence: 97%

“…In addition, 'Students' Expected Occupation Status' also has a positive effect on success. It can be assumed that this variable also increases the motivation of the student and high motivation increases success [6]. ICT Resources, which can be used for the course, have a positive effect on academic success [35].…”

Section: Discussion Conclusion and Recommendationsmentioning

confidence: 99%

“…ICT Resources, which can be used for the course, have a positive effect on academic success [35]. Also, mathematics learning time increase one of the most influential variables on mathematics achievement is the mathematical achievement [6,36]. Furthermore, 'Student Teacher Ratio' and 'Teacher-related Factors Affecting School Climate' [37] have positive effects on mathematical achievement.…”

Section: Discussion Conclusion and Recommendationsmentioning

confidence: 99%

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Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

Şehitoğlu¹,

Serkan²,

Demir³

2022

Cumhuriyet Science Journal

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PISA 2015 mathematical literacy score of Turkey is 420 while the average score of all countries is 461. It is understood that; Turkish students’ PISA 2015 mathematical literacy score was lower than the average. The basic reasons for the below average score need to be truly examined and developmental activities should be revealed. The aim of this study is to classify students according to the factors affecting their mathematical literacy score and to reveal the effects of these factors in classification.The data of the study is obtained from 5895 students who participated in PISA 2015. In this study, we used Random Forest, Naïve Bayes Classifier, Logistic Regression, Decision Tree Algorithm and Discriminant Analysis as classifiers. According to the results, Random Forest method produced more accurate scores than other methods with 76.32% accuracy. We also calculated the correct classification rate and determined the factors that positively and negatively affect the classification with discriminant analysis. According to the discriminant analysis home possessions, information and computer technology resources at home and students' expected occupational status were the most positive effective variables on mathematical literacy score. On the other hand, family wealth possessions, student-related factors affecting school climate and anxiety have negative effect on mathematical literacy score.

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