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Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2020 / Cilt: 11 - Sayı: 4

Comparison of Data Mining Classification Algorithms on Educational Data under Different Conditions

İlhan KOYUNCU
Selahattin GELBAL
Pages
325–345
Publication Date
2020-12-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

While neural networks showed a similar tendency, its overall performance was lower than Naive Bayes and logistic regression.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this study was to examine the performance of Naive Bayes, k-nearest neighborhood, neural networks, and logistic regression analysis in terms of sample size and test data rate in classifying students according to their mathematics performance.
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Key Findings & Data:The target population was 62728 students in the 15-year-old group who were participated in the Programme for International Student Assessment (PISA) in 2012 from The Organisation for Economic Co-operation and Development (OECD) countries.
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Conclusion & Impact:While neural networks showed a similar tendency, its overall performance was lower than Naive Bayes and logistic regression.
🔬Design:Research Article
📍Scope:2012 Verileri
⚙️Variables:Artificial neural networks · educational data mining · k-nearest neighborhood

Abstract

The purpose of this study was to examine the performance of Naive Bayes, k-nearest neighborhood, neural networks, and logistic regression analysis in terms of sample size and test data rate in classifying students according to their mathematics performance. The target population was 62728 students in the 15-year-old group who were participated in the Programme for International Student Assessment (PISA) in 2012 from The Organisation for Economic Co-operation and Development (OECD) countries. The performance of each algorithm was tested by using 11%, 22%, 33%, 44% and 55% of each dataset for small (500 students), medium (1000 students) and large (5000 students) sample sizes. 100 replications were performed for each analysis. As the evaluation criteria, accuracy rates, RMSE values, and total elapsed time were used. RMSE values for each algorithm were statistically compared by using Friedman and Wilcoxon tests. The results revealed that while the classification performance of the methods increased as the sample size increased, the increase of training data ratio had different effects on the performance of the algorithms. The Naive Bayes showed high performance even in small samples, performed the analyzes very quickly, and was not affected by the change in the training data ratio. Logistic regression analysis was the most effective method in large samples but had a poor performance in small samples. While neural networks showed a similar tendency, its overall performance was lower than Naive Bayes and logistic regression. The lowest performances in all conditions were obtained by the k-nearest neighborhood algorithm.

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Cite This Article

KOYUNCU, İ., GELBAL, S. (2020). Comparison of Data Mining Classification Algorithms on Educational Data under Different Conditions. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 11 - Sayı: 4, 325–345. https://ajindex.com/articles/137271

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