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Year 2022, Volume: 43 Issue: 3, 543 - 549, 30.09.2022

Hasan Aykut Karaboğa Serkan Akogul İbrahim Demir

https://doi.org/10.17776/csj.1136733

Abstract

References

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  • [8] Toprak E., Gelbal S., Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis, International Journal of Assessment Tools in Education, 7(4) (2020) 773-799.
  • [9] Shahiri A.M., Husain W., Rashid N.A., A Review on Predicting Student’s Performance Using Data Mining Techniques, Procedia Computer Science, 72 (2015) 414–422.
  • [10] Aksu G., Keceoglu C.R., Comparison of Results Obtained from Logistic Regression, CHAID Analysis and Decision Tree Methods, EJER, 19 (84) (2019) 1–20.
  • [11] Koyuncu İ., Gelbal S., Comparison of Data Mining Classification Algorithms on Educational Data under Different Conditions, Journal of Measurement and Evaluation in Education and Psychology, 11(4) (2020) 325-345.
  • [12] Osmanbegovic E., Suljic M., Data Mining Approach for Predicting Student Performance, Economic Review: Journal of Economics and Business, 10(1) (2012) 3–12.
  • [13] Slater S., Joksimović S., Kovanovic V., Baker R.S., Gasevic D., Tools for Educational Data Mining: A Review, Journal of Educational and Behavioral Statistics, 42(1) (2017) 85–106.
  • [14] Devasia T., Vinushree T.P., Hegde V., Prediction of students performance using Educational Data Mining. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), (2016) 91-95.
  • [15] Tastimur C., Karakose M., Akin E., Improvement of relative accreditation methods based on data mining and artificial intelligence for higher education, 15th International Conference on Information Technology Based Higher Education and Training (ITHET), (2016) 1–7.
  • [16] Gogebakan M., A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method, IEEE Access, 9 (2021) 159987–160003.
  • [17] Agaoglu M., Predicting Instructor Performance Using Data Mining Techniques in Higher Education, IEEE Access, 4(1) (2016) 2379–2387.
  • [18] Tekin A., Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach, EJER, 14(54) (2014) 207–226.
  • [19] Kiray S.A., Gok B., Bozkir A.S., Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods, JESEH, 1(1) (2015) 28.
  • [20] Dolu A., Sosyoekonomik Faktörlerin Eğitim Performansı Üzerine Etkisi: PISA 2015 Türkiye Örneği, Yönetim ve Ekonomi Araştırmaları Dergisi, 18(2) (2020) 41-58.
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  • [25] Bakhshinategh B., Zaiane O.R., ElAtia S., Ipperciel D., Educational data mining applications and tasks: A survey of the last 10 years, Educ Inf Technol, 23(1) (2018) 537–553.
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  • [33] Willmott C. Matsuura K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30 (2015) 79–82.
  • [34] Costa E.B., Fonseca B., Santana M.A., de Araújo F.F., Rego J., Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses, Computers in Human Behavior, 73 (2017) 247–256.
  • [35] Skryabin M., Zhang J., Liu L., Zhang D., How the ICT development level and usage influence student achievement in reading, mathematics, and science, Computers & Education, 85 (2015) 49–58.
  • [36] Martínez-Abad F., Gamazo A., Rodríguez-Conde M.-J., Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment, Studies in Educational Evaluation, 66 (2020) 100875.
  • [37] Topçu M.S., Arıkan S., Erbilgin E., Turkish Students’ Science Performance and Related Factors in PISA 2006 and 2009, Aust. Educ. Res., 42 (1) 117–132.
  • [38] Altun A., Kalkan Ö.K., Cross-national study on students and school factors affecting science literacy, Educational Studies, 47(4) (2021) 403-421.
  • [39] Arends F., Winnaar L., Mosimege M., Teacher classroom practices and Mathematics performance in South African schools: A reflection on TIMSS 2011, South African Journal of Education, 37(3) (2017).
  • [40] Zhao Y., Lu Z., Study on the Application of Multimedia Network Teaching Platform in College Physical Education Teaching, International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(4) (2016) 193–202.

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

Year 2022, Volume: 43 Issue: 3, 543 - 549, 30.09.2022

Hasan Aykut Karaboğa Serkan Akogul İbrahim Demir

https://doi.org/10.17776/csj.1136733

Abstract

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.

Keywords

Classification Educational data mining PISA 2015 Mathematics education Discriminant analysis.

References

  • [1] Taş U.E., Arici Ö., Ozarkan H.B., Özgürlük B., PISA 2015 ulusal raporu, Ankara: Milli Eğitim Bakanlığı, (2016).
  • [2] Witten I.H., Frank E., Data mining: practical machine learning tools and techniques with Java implementations, Acm Sigmod Record, 31(1) (2002) 76-77.
  • [3] Romero C., Ventura S., Educational Data Mining: A Review of the State of the Art, IEEE Trans. Syst., Man, Cybern. C, 40(6) (2010) 601–618.
  • [4] Aksu G., Doğan N., Veri madenciliğinde kullanılan öğrenme yöntemlerinin farklı koşullar altında karşılaştırılması, Ankara University Journal of Faculty of Educational Sciences (JFES), 51(3) (2018) 71-100.
  • [5] Aksu G., Güzeller C.O., Classification of PISA 2012 mathematical literacy scores using Decision-Tree Method: Turkey sampling. Egitim ve Bilim, 41(185) (2016) 101–122.
  • [6] Dos Santos R.A., Paulista C.R., da Hora, H.R.M., Education Data Mining on PISA 2015 Best Ranked Countries: What Makes the Students go Well, Technology, Knowledge and Learning, (2021) 1-32.
  • [7] Martínez Abad F., Chaparro Caso López A.A., Data-mining techniques in detecting factors linked to academic achievement, School Effectiveness and School Improvement, 28(1) (2017) 39–55.
  • [8] Toprak E., Gelbal S., Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis, International Journal of Assessment Tools in Education, 7(4) (2020) 773-799.
  • [9] Shahiri A.M., Husain W., Rashid N.A., A Review on Predicting Student’s Performance Using Data Mining Techniques, Procedia Computer Science, 72 (2015) 414–422.
  • [10] Aksu G., Keceoglu C.R., Comparison of Results Obtained from Logistic Regression, CHAID Analysis and Decision Tree Methods, EJER, 19 (84) (2019) 1–20.
  • [11] Koyuncu İ., Gelbal S., Comparison of Data Mining Classification Algorithms on Educational Data under Different Conditions, Journal of Measurement and Evaluation in Education and Psychology, 11(4) (2020) 325-345.
  • [12] Osmanbegovic E., Suljic M., Data Mining Approach for Predicting Student Performance, Economic Review: Journal of Economics and Business, 10(1) (2012) 3–12.
  • [13] Slater S., Joksimović S., Kovanovic V., Baker R.S., Gasevic D., Tools for Educational Data Mining: A Review, Journal of Educational and Behavioral Statistics, 42(1) (2017) 85–106.
  • [14] Devasia T., Vinushree T.P., Hegde V., Prediction of students performance using Educational Data Mining. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), (2016) 91-95.
  • [15] Tastimur C., Karakose M., Akin E., Improvement of relative accreditation methods based on data mining and artificial intelligence for higher education, 15th International Conference on Information Technology Based Higher Education and Training (ITHET), (2016) 1–7.
  • [16] Gogebakan M., A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method, IEEE Access, 9 (2021) 159987–160003.
  • [17] Agaoglu M., Predicting Instructor Performance Using Data Mining Techniques in Higher Education, IEEE Access, 4(1) (2016) 2379–2387.
  • [18] Tekin A., Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach, EJER, 14(54) (2014) 207–226.
  • [19] Kiray S.A., Gok B., Bozkir A.S., Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods, JESEH, 1(1) (2015) 28.
  • [20] Dolu A., Sosyoekonomik Faktörlerin Eğitim Performansı Üzerine Etkisi: PISA 2015 Türkiye Örneği, Yönetim ve Ekonomi Araştırmaları Dergisi, 18(2) (2020) 41-58.
  • [21] Güre Ö.B., Kayri M., Erdoğan F., Analysis of Factors Effecting PISA 2015 Mathematics Literacy via Educational Data Mining, Education & Science/Egitim ve Bilim, 45(202) (2020).
  • [22] Tan P.N., Steinbach M., Kumar V., Introduction to data mining, Second edition, Global edition, New York, (2020).
  • [23] Romero C., Ventura S., Data mining in education, WIREs Data Mining and Knowledge Discovery, 3(1) (2013) 12–27.
  • [24] Peña-Ayala A., Educational data mining: A survey and a data mining-based analysis of recent works, Expert Systems with Applications, 41(4) (2014) 1432–1462.
  • [25] Bakhshinategh B., Zaiane O.R., ElAtia S., Ipperciel D., Educational data mining applications and tasks: A survey of the last 10 years, Educ Inf Technol, 23(1) (2018) 537–553.
  • [26] Chong S., Mak M., Loh W.M., Data-mining applications with the admission data of adult learners in higher education: a pilot study, IJMIE, 10(2) (2016) 131.
  • [27] Mishra T., Kumar D., Gupta S., Students’ Performance and Employability Prediction through Data Mining: A Survey, Indian Journal of Science and Technology, 10(24) (2017) 1-6.
  • [28] Izenman A.J., Modern multivariate statistical techniques: regression, classification, and manifold learning. New York, Springer, (2008).
  • [29] Quinlan J.R., C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann Publishers, (1993).
  • [30] Hosmer Jr D.W., Lemeshow S., Sturdivant R.X., Applied logistic regression, John Wiley & Sons, 398 (2013).
  • [31] Donner A. Klar N., The statistical analysis of kappa statistics in multiple samples, Journal of Clinical Epidemiology, 49(9) (1996) 1053–1058.
  • [32] Turanoglu-Bekar E., Ulutagay G., Kantarcı-Savas S., Classification of Thyroid Disease by Using Data Mining Models: A Comparison of Decision Tree Algorithms, The Oxford Journal of Intelligent Decision and Data Science, 2016(2) (2016) 13–28.
  • [33] Willmott C. Matsuura K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30 (2015) 79–82.
  • [34] Costa E.B., Fonseca B., Santana M.A., de Araújo F.F., Rego J., Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses, Computers in Human Behavior, 73 (2017) 247–256.
  • [35] Skryabin M., Zhang J., Liu L., Zhang D., How the ICT development level and usage influence student achievement in reading, mathematics, and science, Computers & Education, 85 (2015) 49–58.
  • [36] Martínez-Abad F., Gamazo A., Rodríguez-Conde M.-J., Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment, Studies in Educational Evaluation, 66 (2020) 100875.
  • [37] Topçu M.S., Arıkan S., Erbilgin E., Turkish Students’ Science Performance and Related Factors in PISA 2006 and 2009, Aust. Educ. Res., 42 (1) 117–132.
  • [38] Altun A., Kalkan Ö.K., Cross-national study on students and school factors affecting science literacy, Educational Studies, 47(4) (2021) 403-421.
  • [39] Arends F., Winnaar L., Mosimege M., Teacher classroom practices and Mathematics performance in South African schools: A reflection on TIMSS 2011, South African Journal of Education, 37(3) (2017).
  • [40] Zhao Y., Lu Z., Study on the Application of Multimedia Network Teaching Platform in College Physical Education Teaching, International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(4) (2016) 193–202.
There are 40 citations in total.

Details

Primary Language English
Subjects Statistics
Journal Section Natural Sciences
Authors

Hasan Aykut Karaboğa 0000-0001-8877-3267

Serkan Akogul 0000-0002-0346-4308

İbrahim Demir 0000-0002-2734-4116

Publication Date September 30, 2022
Submission Date June 27, 2022
Acceptance Date September 12, 2022
Published in Issue Year 2022Volume: 43 Issue: 3

Cite

APA Karaboğa, H. A., Akogul, S., & Demir, İ. (2022). Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application. Cumhuriyet Science Journal, 43(3), 543-549. https://doi.org/10.17776/csj.1136733
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