GELİŞTİRİLMİŞ YAPAY SİNİR AĞLARI (ANN) VE ÇOKLU DOGRUSAL REGRESYON (MLR) MODELLERİYLE ÇOCUKLARDA BİLGİSAYAR OYUN BAĞIMLILIĞININ TAHMİN EDİLMESİ

Yıl 2021, Cilt: 23 Sayı: 2, 551 - 570, 24.12.2021

Esma Uzunhisarcıklı , E Kavuncuoglu , Hanife Akgül

https://doi.org/10.26468/trakyasobed.789767

Öz

Çocuklarda oyun bağımlılığının tahmini, çocuğun zihinsel ve fiziksel gelişiminde büyük rol oynar. Bu nedenle çocukların oyun bağımlılığını incelemek için çeşitli ölçekler kullanılmış ve ölçeklerde çeşitli girdi parametreleri (Yaş, Cinsiyet, Günlük Oyun Süresi vb.) kullanılmıştır. Bu çalışmanın amacı, girdi parametrelerine bakıldığında çocuğun oyuna bağımlı olup olmadığını tahmin eden bir uzman sistemi tasarlamaktır. Bu sistemin tasarlanması amacıyla iki model kullanılmıştır. Bu modellerden biri Yapay Sinir Ağları (YSA) ile diğer ise Çoklu Doğrusal Regresyon (ÇDR) ile geliştirilmiştir. Modellerin performansı, Kök Ortalama Kare Hatası (KOKH) ve Korelasyon Katsayısı (R) kriterleri kullanılarak değerlendirilmiştir. Bu kriterler analiz edildiğinde, YSA yüksek tahmin performansı gösterirken, MLR düşük tahmin performansı göstermiştir. Sonuç olarak, YSA ile geliştirilen sisteme farklı girdi değerleri verildiğinde, çocuklardaki oyun bağımlılığı ile ilgili en doğru tahminlerin elde edildiği görülmüştür.

Anahtar Kelimeler

Yapay sinir ağları, Çoklu Doğrusal Regresyon, Bilğisayar Oyun Bağımlılığının Tahmini, Uzman Sistem

PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS

Öz

Estimation of game addiction in children plays a major role in the mental and physical development of the child. Therefore, Various scales are used to examine game addiction of children and various input parameters (Age, Gender, Daily play time, etc.) are employed in scales. The purpose of this study is to project a system that estimates whether the child is addicted to the game when looking at the input parameters. Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) techniques were used to design this system. In order to measure the predictive performance of the developed models, the Root Mean Squared Error (RMSE), and Correlation Coefficient (R) criteria were examined respectively and it was observed that the model developed by ANN predicted CGA with high accuracy.

Anahtar Kelimeler

Artificial Neural Network, Multiple Linear Regression, Prediction of Computer Game Addiction, Expert System

Kaynakça

• Akçakaya, Ü. (2013). The hidden hazard of our era “Computer Game Addiction”. Retrieved April 15, 2016 from http://blog.milliyet.com.tr/cagimizin-gizli-tehlikesi---bilgisayar-oyunu-bagimliligi-/Blog/?BlogNo=396377 (Date of access: 15.03.2017)
• Azadi, S., Sepaskhah, A.R., (2011). Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theor. Appl. Climatol
• Azadi S., Karimi-Jashni A., (2016). Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran. Waste Management 48 (2016) 14–23
• Bayraktutan, F. (2005). The internet use in terms of family relations. Unpublished postgraduate thesis, Istanbul University, Institute of Social Sciences, Department of Social Structure– Social Change, Istanbul.
• Beale, M.H., Hagan, M.T., Demuth, M.H., (2010). Neural Network ToolboxTM 7 User´s Guide. MathWorks Inc, Natick, MA
• Chumbley, J., & Griffiths, M. (2006). Affect and the computer game player: The effect of gender, personality, and game reinforcement structure on affective responses to computer gameplay.
• Cyberpsychology & Behavior, 9(3), 308–316. http://online.liebertpub.com/doi/pdf/10.1089/cpb.2006.9.308 (Date of Access: 17.03.2017)
• Çakır, Ö., Horzum, M.B. and Ayas, T. (2013). Definition and History of Internet Addiction. In M. Kalkan & C. Kaygusuz (Ed.), Internet addiction-related problems and solutions (pp. 1-16). Ankara: Anı Publishing.
• Doğan, İ. (2000). Concepts and Problems of Sociology (3rd Edition). Istanbul: Sistem Publishing.
• Erkan, H. (1997). Information Society and Economic Development (3rd Edition), Istanbul, İş Bankası Kültür Yayınları.
• Fidan, E.K, Pekşen Akça, R. and Akgül, H. (2016). Opinions of Adolescents on Candy Crush Saga Game: A Qualitative Study. Journal of Academic Sight, 55, 700-715.
• Flott, L.W., (2012). Using the scatter diagram tool to compare data, show relationship. Met. Finish. 110, 33–35
• Fukuoka, Y., Matsuki, H., Minamitani, H., Ishida, A., 1998. A modified backpropagation method to avoid false local minima. Neural Netw. 11, 1059–1072.
• Günüç, S. And Kayri M. (2010). “The Profile of Internet Addiction in Turkey and Development of Internet Addiction Scale: Validity- Reliability Study”, H. U. Journal of Education, 39, 220-232.
• file:///C:/Users/akg%C3%BCl/Desktop/internet-bagimlilik-olcegitoad%202. Pdf (Date of Access: 02.01.2017)
• Horzum, M.B. (2011). Examination of computer game addition levels of primary school students in terms of various variables. Education and Science, 36(159),56-68.
• Horzum, M.B., Ayas, T. and Balta, Ö.Ç. (2008). The scale of computer game addition for children. Turkish PDR (Psychological Counseling and Guidance) Journal, 3(30), 76-88.
• https://en.wikipedia.org/wiki/Linear_regression (Date of Access: 05.03.2016)
• Jahandideh, S., Jahandideh, S., Asadabadi, E., Askarian, M., Movahedi, M.M., Hosseini, S., Jahandideh, M., (2009). The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. J. Waste Manage. 29, 2874–2879.
• Karaman, M.K. and Kurtoğlu, M. (2009) Opinions of preservice teacher on internet addiction, Academic Information’09 - XI. Academic Information conference, 11-13 February, Harran University, Şanlıurfa.
• Karasar, N. (1995). Scientific research method- concepts, principles, techniques: 7th Edition. Ankara: 3A
• Kaya, A.B. (2013). Development of Online game addiction scale: validity and reliability study. (Unpublished postgraduate thesis). Gaziosmanpaşa University/Institute of Educational Sciences, Tokat.
• Kocadağlı, O., (2015). A novel hybrid learning algorithm for full Bayesian approach of artificial neural networks. Appl. Soft Comput. 35, 52–65.
• Leung, L. (2004). Net-Generation Attributes and Seductive Properties of the Internet as Predictors of Online Activities and Internet Addiction. Cyberpsychology & Behavior, 7(3).
• Makridakis, S., Hibon, M., (1995). Evaluating accuracy (or error) measures. Printed at INSEAD, Fontainebleau, France.
• Nirun, N. (1994). Family and Culture in terms of systematic Sociology, Ankara: Publication of Atatürk Cultural Center.
• Rapeepisarn, K., Wong, K. W., Fung, C.C., & Khine, M. S. (2008). The relationship between game genres, learning techniques and learning styles in educational computer games. LNCS, 5093, 497–508.
• Shu, Y., Lam, N.S.N., (2011). Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model. Atmos. Environ. 45, 634–640.
• Şahin, C and Tuğrul, V.M.(2012). Examination of computer game addiction levels of primary school students. Zeitschrift für die Welt der Türken, 4(3), 115-130.
• TDK, (2016). Current Turkish Dictionary. Retrieved April 15, 2016 from http://www.tdk.gov.tr/ (Date of Access: 01.05.2017)
• Tina, G.M., De Fiore, S., Ventura, C., (2012). Analysis of forecast errors for irradiance on the horizontal plane. Energy Convers. Manage. 64, 533–540.
• Wang, Y., Li, J., Gu, J., Zhou, Z., Wang, Z., (2015). Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl. Soft Comput. 35, 280–290
• Yalçın., N. (2006). Do We Use Internet Properly? Are you Internet Addict? Are our Children and Adolescents at Risk?, 9 - 11 February Pamukkale University Congress of Information Technologies IV Academic Information Proceedings Book, 85-588, Denizli.
• Zhang, G., Patuwo, B.E., Hu, M.Y., (1998). Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14, 35–62.
• Zhao, X., Wang, sh., Li, T., (2011). Review of evaluation criteria and main methods of wind power forecasting. Energy Proc. 12, 761–769.