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Bir Öğrenciyi Üstün Zekâlı ve Yetenekli Olarak Aday Göstermek İçin Doğru Soruları Sormak: Bir Makine Öğrenmesi Yaklaşımı

Year 2020, Volume 13, Issue 4, 385 - 400, 30.10.2020
https://doi.org/10.17671/gazibtd.591158

Abstract

Bu çalışmada, bir öğrencinin üstün zekâlı ve yetenekli olarak aday gösterilmesi için geliştirilen 69 soruluk ölçekten öğretmenin kararında en etkili soruların seçilerek ölçekteki soru sayısının azaltılması amaçlanmıştır. Bu amaçla Nitelik Eleme ve Ki-kare Filtresi nitelik seçimi yöntemleri kullanılmıştır. Ayrıca çalışmada bir öğrenciyi üstün zekâlı ve yetenekli olarak aday göstermede en iyi performansı veren makine öğrenmesi algoritmasının bulunması da hedeflenmiştir. Bunu gerçekleştirebilmek için Rastgele Orman Algoritması, C4.5 Karar Ağacı Algoritması ve Naive Bayes Sınıflandırıcı makine öğrenmesi algoritmaları kullanılmıştır. Analizler sonucunda Ki-kare Filtresi yöntemiyle 69 soruluk ölçek 20 soruya indirilmiş, sonrasında Naive Bayes Sınıflandırıcı bu yeni veri setine uygulandığında, model %92 doğrulukla bir öğrenciyi üstün zekâlı ve yetenekli olarak aday göstermiştir. Önerilen bu modelin, aday gösterme sürecinde zamandan tasarruf edilmesini sağlayacağı ve ölçeğin öğretmenler tarafından doldurulması esnasında çok sayıda soruyla ilgilenmekten kaynaklı dikkat dağınıklığını önleyerek sonuçların doğruluğunu artıracağı düşünülmektedir. Ayrıca, veriye dayalı öngörü modellerinin aday gösterme sürecinde kullanılmasıyla daha rasyonel kararlar elde edileceğine inanılmaktadır.

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Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach

Year 2020, Volume 13, Issue 4, 385 - 400, 30.10.2020
https://doi.org/10.17671/gazibtd.591158

Abstract

In this study, it is aimed to reduce the number of questions from a 69-item scale, which is developed to nominate a student as gifted and talented by selecting the most effective questions. For this purpose, Recursive Feature Elimination and Chi-Square Filter feature selection methods are used. Also, it is aimed to find the best performing machine learning algorithm to nominate a student as gifted and talented in this study. To achieve this, analyses are performed with Random Forest Algorithm, C4.5 Decision Tree Algorithm, and Naive Bayes Classifier machine learning algorithms. As a result of the analyses; the 69-item scale was reduced to 20 questions by using Chi-Square Filter method, and then when Naive Bayes Classifier was applied to this new data set, the model nominated a student with 92% accuracy as gifted and talented. It is thought that the proposed model will save time in the nomination process and prevent the distraction of attention that can be caused by the high number of questions when teachers fill out the scale. Also, it is believed that more rational decisions will be made in the nomination process by working with data-based prediction models.

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Details

Primary Language English
Subjects Computer Science, Information System
Journal Section Articles
Authors

Elif KARTAL> (Primary Author)
İstanbul University, Informatics Department
0000-0003-4667-1806
Türkiye


Melodi ÖZYAPRAK>
İstanbul University-Cerrahpaşa, Hasan Ali Yücel Faculty of Education, Department of Special Education
0000-0003-1891-8218
Türkiye


Zeki ÖZEN>
İstanbul University, Informatics Department
0000-0001-9298-3371
Türkiye


İrfan ŞİMŞEK>
İstanbul University-Cerrahpaşa, Hasan Ali Yücel Faculty of Education, Department of Computer Education and Instructional Technologies
0000-0002-7481-5830
Türkiye


Sezer KÖSE BİBER>
İstanbul University-Cerrahpaşa, Hasan Ali Yücel Faculty of Education, Department of Computer Education and Instructional Technologies
0000-0001-5807-5185
Türkiye


Mahir BİBER>
İstanbul University-Cerrahpaşa, Hasan Ali Yücel Faculty of Education, Department of Mathematics and Science Education
0000-0003-4044-6966
Türkiye


Tuncer CAN>
İstanbul University-Cerrahpaşa, Hasan Ali Yücel Faculty of Education, Depatment of English Language Teaching
0000-0001-8145-0772
Türkiye

Supporting Institution Scientific Research Projects Coordination Unit of İstanbul University
Project Number 23538 and 26087
Thanks This study was supported by Scientific Research Projects Coordination Unit of İstanbul University. Project numbers 23538 and 26087
Publication Date October 30, 2020
Published in Issue Year 2020, Volume 13, Issue 4

Cite

Bibtex @research article { gazibtd591158, journal = {Bilişim Teknolojileri Dergisi}, issn = {1307-9697}, eissn = {2147-0715}, address = {}, publisher = {Gazi University}, year = {2020}, volume = {13}, number = {4}, pages = {385 - 400}, doi = {10.17671/gazibtd.591158}, title = {Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach}, key = {cite}, author = {Kartal, Elif and Özyaprak, Melodi and Özen, Zeki and Şimşek, İrfan and Köse Biber, Sezer and Biber, Mahir and Can, Tuncer} }
APA Kartal, E. , Özyaprak, M. , Özen, Z. , Şimşek, İ. , Köse Biber, S. , Biber, M. & Can, T. (2020). Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach . Bilişim Teknolojileri Dergisi , 13 (4) , 385-400 . DOI: 10.17671/gazibtd.591158
MLA Kartal, E. , Özyaprak, M. , Özen, Z. , Şimşek, İ. , Köse Biber, S. , Biber, M. , Can, T. "Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach" . Bilişim Teknolojileri Dergisi 13 (2020 ): 385-400 <https://dergipark.org.tr/en/pub/gazibtd/issue/57610/591158>
Chicago Kartal, E. , Özyaprak, M. , Özen, Z. , Şimşek, İ. , Köse Biber, S. , Biber, M. , Can, T. "Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach". Bilişim Teknolojileri Dergisi 13 (2020 ): 385-400
RIS TY - JOUR T1 - Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach AU - Elif Kartal , Melodi Özyaprak , Zeki Özen , İrfan Şimşek , Sezer Köse Biber , Mahir Biber , Tuncer Can Y1 - 2020 PY - 2020 N1 - doi: 10.17671/gazibtd.591158 DO - 10.17671/gazibtd.591158 T2 - Bilişim Teknolojileri Dergisi JF - Journal JO - JOR SP - 385 EP - 400 VL - 13 IS - 4 SN - 1307-9697-2147-0715 M3 - doi: 10.17671/gazibtd.591158 UR - https://doi.org/10.17671/gazibtd.591158 Y2 - 2020 ER -
EndNote %0 Journal of Information Technologies Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach %A Elif Kartal , Melodi Özyaprak , Zeki Özen , İrfan Şimşek , Sezer Köse Biber , Mahir Biber , Tuncer Can %T Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach %D 2020 %J Bilişim Teknolojileri Dergisi %P 1307-9697-2147-0715 %V 13 %N 4 %R doi: 10.17671/gazibtd.591158 %U 10.17671/gazibtd.591158
ISNAD Kartal, Elif , Özyaprak, Melodi , Özen, Zeki , Şimşek, İrfan , Köse Biber, Sezer , Biber, Mahir , Can, Tuncer . "Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach". Bilişim Teknolojileri Dergisi 13 / 4 (October 2020): 385-400 . https://doi.org/10.17671/gazibtd.591158
AMA Kartal E. , Özyaprak M. , Özen Z. , Şimşek İ. , Köse Biber S. , Biber M. , Can T. Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach. Bilişim Teknolojileri Dergisi. 2020; 13(4): 385-400.
Vancouver Kartal E. , Özyaprak M. , Özen Z. , Şimşek İ. , Köse Biber S. , Biber M. , Can T. Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach. Bilişim Teknolojileri Dergisi. 2020; 13(4): 385-400.
IEEE E. Kartal , M. Özyaprak , Z. Özen , İ. Şimşek , S. Köse Biber , M. Biber and T. Can , "Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach", Bilişim Teknolojileri Dergisi, vol. 13, no. 4, pp. 385-400, Oct. 2020, doi:10.17671/gazibtd.591158