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Journals / IOJET / 2020 / Cilt: 7 - Sayı: 4

COMPARISON OF MATLAB AND SPSS SOFTWARE IN THE PREDICTION OF ACADEMIC ACHIEVEMENT WITH ARTIFICIAL NEURAL NETWORKS: MODELING FOR ELEMENTARY SCHOOL STUDENTS

Mustafa ÇEVİK
Gizem TABARU- ÖRNEK
Journal
IOJET
Pages
1689–1707
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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

It was found that MATLAB software had a higher rateof the correct prediction compared to SPSS.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it was aimed to compare the predictions of the academic achievement of theartificial neural networks (ANN) run in MATLAB and SPSS software and to determine thefactors related to their academic achievement.
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Key Findings & Data:Sample consisted of 465 students who werestudying at Grade 4 in primary schools in the Central Anatolian Region of Turkey in 2017.
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Conclusion & Impact:It was found that MATLAB software had a higher rateof the correct prediction compared to SPSS.
🔬Design:Survey & Empirical Study
📍Scope:2017 Verileri
⚙️Variables:Domain Parameters

Abstract

In this study, it was aimed to compare the predictions of the academic achievement of theartificial neural networks (ANN) run in MATLAB and SPSS software and to determine thefactors related to their academic achievement. Sample consisted of 465 students who werestudying at Grade 4 in primary schools in the Central Anatolian Region of Turkey in 2017. A12-questions questionnaire was used as the collection tool. For the content validity of thequestionnaire, expert opinions were received. The KR20 reliability coefficient was calculatedas .60. An exploratory factor analysis was run for the construct validity. In the ANN model, theitems related to the academic achievement in the questionnaire were considered as independentvariables / inputs, and the academic achievements of the previous year as the dependentvariables / outputs. The predictions of the academic achievement of the ANN models wereanalyzed in MATLAB R2013a and SPSS 24.0 software and the regression coefficients of theindependent variables were examined. It was found that MATLAB software had a higher rateof the correct prediction compared to SPSS. In the regression coefficients of the independentvariables, some differences and similarities between the results of MATLAB and SPSS werefound.

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Cite This Article

ÇEVİK, M., ÖRNEK, G. T. (2022). COMPARISON OF MATLAB AND SPSS SOFTWARE IN THE PREDICTION OF ACADEMIC ACHIEVEMENT WITH ARTIFICIAL NEURAL NETWORKS: MODELING FOR ELEMENTARY SCHOOL STUDENTS. IOJET, Cilt: 7 - Sayı: 4, 1689–1707. https://ajindex.com/articles/542217

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References (13)

  1. Alamdar, S. (2015). Analysing the elements affecting academic success in the context of efficient school concept. Unpublished master thesis. Ankara Üniversity, Ankara.
  2. Altaş, D., & Gülpınar, V. (2012). A comparison of classification performances of the decision trees and the artificial neural networks: European Union. Journal of Trakya University Social Sciences, 14(1), 1-22.
  3. Anıl, D. (2009). Factors effecting science achievement of science students in Programme for International Students’ Achievement (PISA) in Turkey. Education ve Science, 34(152), 15-34.
  4. Atasayar, A. (2019). Predicting primary school LGS achievement with artificial neural networks. Unpublished master thesis. Kutahya Dumlupinar University, Kutahya. Aydoğan, İ. (2018). Estimation of student successes by artificial neural networks. YYU Journal of Education Faculty, 15(1), 577-610.
  5. Ayık, Y. Z., Özdemir, A., & Yavuz, U. (2007). Lise türü ve mezuniyet başarısının kazanılan fakülte ile ilişkisinin veri madenciliği tekniği ile analizi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 10(2), 441-454.
  6. Bahadır, E. (2013). Prediction of student teachers' academic success with logistic regression analysis and artificial neural networks methods. Unpublished Doktoral dissertation, Marmara University. İstanbul.
  7. Bahadır, E. (2016). Using neural network and logistic regression analysis to predict prospective mathematics teachers' academic success upon entering graduate education. Educational Sciences: Theory & Practice, 16(3). 943-964.
  8. Bekele, R., & McPherson, M. (2011). A Bayesian performance prediction model for mathematics education: A prototypical approach for effective group composition. British Journal of Educational Technology, 42(3), 395–416.
  9. Boukadıda, H., Hassen N., Gafsı, Z., & Besbes, K. (2011). A highly time-efficient digital multiplier based on the A2 binary representation. International Journal of Engineering Science and Tecnology, 3(5), 4498-4509.
  10. Budak, H. & Erpolat, S. (2012). Comparison of artificial neural networks and logistic regression analysis in the credit risk prediction. Online Academic Journal of Information Technology, 3(9), 23-30.
  11. Büyüköztürk, Ş. (2009). Data analysis handbook for Social Sciences. Ankara:
  12. Pegem Akademi. Cameron, S., & Heckman, J. (2001). The dynamics of educational attainment for black. hispanic. and white males. Journal of Political Economy, 109(3), 455-499.
  13. Campbell Hunt, C. (2000). What have we learned about generıccompetıtıve strategy? A metaanalys