TY  - JOUR
T1  - Application of Data Mining Algorithms and Statistical Hypothesis Testing to Analyze Problematic Internet Use among University Students
TT  - Üniversite Öğrencilerinde Problemli İnternet Kullanımını Analiz Etmek İçin Veri Madenciliği Algoritmalarının ve İstatistiksel Hipotez Testinin Uygulanması
AU  - Kaya Keleş, Mümine
AU  - Özer, Elife
AU  - Özer, Ömer
PY  - 2025
DA  - March
Y2  - 2025
DO  - 10.31466/kfbd.1537843
JF  - Karadeniz Fen Bilimleri Dergisi
JO  - KFBD
PB  - Giresun Üniversitesi
WT  - DergiPark
SN  - 2564-7377
SP  - 248
EP  - 271
VL  - 15
IS  - 1
LA  - en
AB  - This study aims to understand university students' internet usage habits and whether these habits are associated with certain sociodemographic and behavioral variables. Data were obtained from the General Problematic Internet Use Scale 2 (GPIUS2) survey, administered to undergraduate students from eight programs at a state university in Türkiye, and analyzed using the Apriori Association Rule Mining Algorithm and statistical hypothesis testing. The responses from 217 students were analyzed to gain insights into their problematic internet use habits. Analysis conducted using WEKA software identified significant associations with the "Mood Regulation" dimension, particularly among male students and those with high GPAs. Additionally, communication-related smartphone usage was found to be associated with the "Negative Outcomes" dimension. The research findings also reveal that university students' problematic internet use is statistically associated with the program they are enrolled in, marital status, self-reported daily smartphone usage, primary reason for smartphone use, and self-reported addiction. This study contributes a new perspective to the application of data mining techniques in social sciences and educational research, providing valuable insights into the relationships between internet usage habits and problematic usage, thus laying a foundation for future research.
KW  - Association Rule Mining
KW  - Data Mining
KW  - Technological Addiction Analysis
KW  - Problematic Internet Use
KW  - University Students
N2  - Bu çalışma, üniversite öğrencilerinin internet kullanım alışkanlıklarını ve bu alışkanlıkların belirli sosyodemografik ve davranışsal değişkenlerle ilişkili olup olmadığını anlamayı amaçlamaktadır. Veriler, Türkiye'deki bir devlet üniversitesinin sekiz programında öğrenim gören lisans öğrencilerine uygulanan Genelleştirilmiş Problemli İnternet Kullanımı Ölçeği 2 (GPIUS2) anketinden elde edilmiş ve Apriori Birliktelik Kuralı Madenciliği Algoritması ile istatistiksel hipotez testleri kullanılarak analiz edilmiştir. Toplamda 217 öğrencinin yanıtları, problemli İnternet kullanım alışkanlıklarına dair sonuçlar elde etmek amacıyla analiz edilmiştir. WEKA yazılımı kullanılarak yapılan analizler, özellikle erkek öğrenciler ve yüksek not ortalamasına sahip öğrenciler arasında "Duygu Düzenleme" boyutuyla anlamlı ilişkiler tespit etmiştir. Ayrıca, iletişim amaçlı telefon kullanımının "Olumsuz Sonuçlar" boyutuyla ilişkili olduğu bulunmuştur. Araştırmanın istatiksel hipotezlere ilişkin bulguları ise, üniversite öğrencilerinin problemli internet kullanımının kayıtlı oldukları program, medeni durum, kendi bildirdikleri günlük akıllı telefon kullanımı, akıllı telefon kullanımının birincil nedeni ve kendi bildirdikleri bağımlılık durumu ile istatistiksel olarak ilişkili olduğunu ortaya koymaktadır. Bu çalışma, veri madenciliği tekniklerinin sosyal bilimler ve eğitim araştırmalarında uygulanmasına yeni bir perspektif kazandırarak, internet kullanım alışkanlıkları, problemli İnternet kullanım ve sosyodemografik özellikler ve akademik başarı arasındaki ilişkiler hakkında kapsamlı çıkarımlar sunmakta ve böylelikle gelecekteki araştırmalara temel oluşturmaktadır.
CR  - Agrawal, R., and Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487-499.
CR  - Ak, S., Koruklu, N., and Yılmaz, Y. (2013). A study on Turkish adolescent's Internet use: Possible predictors of Internet addiction. Cyberpsychology, behavior and social networking, 16(3), 205–209. https://doi.org/10.1089/cyber.2012.0255
CR  - Alqarni, A. M., Boubshait, L. A., Alnufaily, D. A., Alalwan, S. J., Fardan, H. R. A., Alabdulmhsin, M. F., Alotaibi, M. A., and Boubshait, N. A. (2024). Prevalence of internet addiction and its association with psychological disorders among medical students in Saudi Arabia. Psychology Research and Behavior Management, 17, 2747-2755.
CR  - Baloğlu, M., Şahin, R., and Arpaci, I. (2017). A review of recent research in problematic internet use: gender and cultural differences. Current Opinion in Psychology, 36, 124-129. https://doi.org/10.1016/j.copsyc.2020.05.008
CR  - Baltacı, S., and Ersoz, A. R. (2022). Social media engagement, fear of missing out and problematic internet 
use in secondary school children. International Online Journal of Educational Sciences, 14(1), 197-210.
CR  - Bergmark, K. H., Bergmark, A., and Findahl, O. (2011). Extensive internet involvement--addiction or emerging lifestyle?. International Journal of Environmental Research and Public Health, 8(12), 4488–4501. https://doi.org/10.3390/ijerph8124488
CR  - Bhardwaj, A., Tiwari, A., and Tiwari, V. (2018a). Association rule mining for medical image diagnosis. International Journal of Information Technology, 10(2), 153-157.
CR  - Bhardwaj, A., Tiwari, A., and Agrawal, S. (2018b). Mining association rules from medical data using Apriori algorithm. International Journal of Computer Applications, 180(6), 28-33.
CR  - Brino, K. A. S., Derouin, A. L., and Silva, S. G. (2022). Problematic internet use in adolescents and implementation of a social media hygiene protocol. Journal of Pediatric Nursing, 63, 84-89. https://doi.org/10.1016/j.pedn.2021.10.011
CR  - Cao, H., Sun, Y., Wan, Y., Hao, J., and Tao, F. (2011). Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health, 11. https://doi.org/10.1186/1471-2458-11-802
CR  - Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive-behavioral measure. Computers in Human Behavior, 18(5), 533–575. https://doi.org/10.1016/S0747-5632(02)00004-3
CR  - Caplan, S. E. (2010). Theory and measurement of generalized problematic Internet use: A two-step approach. Computers in Human Behavior, 26(5), 1089-1097.
CR  - Chen, Y., Liu, Z., and Wu, Q. (2018). Applying Apriori Algorithm to Analyze Student Interaction Data in E-Learning Platforms. International Journal of Learning Technology, 13(4), 321-338.
CR  - Çiçek, E., Uçar, M. T., and Öztürk, M. (2023). Risk factors associated with problematic Internet use in high school students: A nested case–control study. Addicta: The Turkish Journal on Addictions, 10(2), 115-126
CR  - Das, A., Jana, S., Ganguly, P., and Chakraborty, N. (2021). Application of association rule: Apriori algorithm in E-Commerce. In 2021 Innovations in Energy Management and Renewable Resources (52042) (pp. 1-7). IEEE. https://doi.org/10.1109/IEMRE52042.2021.9386737
CR  - Derbyshire, K. L., Lust, K. A., Schreiber, L. R., Odlaug, B. L., Christenson, G. A., Golden, D. J., and Grant, J. E. (2013). Problematic Internet use and associated risks in a college sample. Comprehensive Pychiatry, 54(5), 415–422. https://doi.org/10.1016/j.comppsych.2012.11.003
CR  - Fatehi, F., Monajemi, A., Sadeghi, A., Mojtahedzadeh, R., and Mirzazadeh, A. (2016). Quality of life in medical students with internet addiction. Acta Medica Iranica, 54(10), 662-666.
CR  - Han, J., and Kamber, M. (2001). Data mining: Concepts and techniques. Morgan Kaufinann.
CR  - Haque, M., Rahman, N. A. A., Majumder, M. A. A., Haque, S. Z., Kamal, Z. M., Islam, Z., Haque, A. E., Rahman, N. I. A., and Alattraqchi, A. G. (2016). Internet use and addiction among medical students of Universiti Sultan Zainal Abidin, Malaysia. Psychology Research and Behavior Management, 9, 297-307.
CR  - Hashemi, A., Noori, A. Q., Orfan, S. N., Akramy, S. A., and Mohd Rameli, M. R. (2024). Undergraduate students’ perception of smartphone addiction and its impact on themselves and their academic performance: a case study. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2340845
CR  - Houston, M. L., and Foubert, J. (2024). The relationship between internet addiction test scores and academic achievement. Growth: The Journal of the Association for Christians in Student Development, 23(23)
CR  - Keleş, A. E., and Kaya, M. (2014). Duvar inşa edilmesinde verimliliği etkileyen faktörlerin Apriori veri madenciliği yöntemi kullanılarak analizi. Akademik Bilişim Konferansı, AB2014 Bildiriler Kitapçığı, 831-836.
CR  - Kutluay, E., and Karaca, F. (2024). A model proposal explaining the influence of smartphone addiction related factors on high school students’ academic success. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12947-x
CR  - Liu, B., Hsu, W., and Ma, Y. (1998). Integrating classification and association rule mining, Proc. Fourth Int’l Conf. Knowledge Discovery and Data Mining (KDD ‘98), 80–86.
CR  - Liu, Y., Wang, Q., and Shi, X. (2019a). An Apriori-based algorithm for mining association rules in education data. Journal of Educational Technology Development and Exchange, 12(1), 1-13.
CR  - Liu, Y., Li, H., and Zhao, J. (2019b). Application of Apriori algorithm in analyzing learning behaviors and academic performance. Journal of Educational Technology Development and Exchange, 12(2), 45-60.
CR  - Mrayyan, M. T., Al-Rawashdeh, S., Khait, A. A., and Rababa, M. (2022). Differences in Cyberchondria, Internet Addiction, Anxiety Sensitivity, Health Anxiety, and Coronavirus Anxiety Among Students: A Web-Based Comparative Survey. Electronic Journal of General Medicine, 19(3), 1-8. https://doi.org/10.29333/ejgm/11876
CR  - Özgür, H., Demiralay, T., and Demiralay, İ. (2014). Exploration of problematic Internet use and loneliness among distance education students. Turkish Online Journal of Distance Education, 15(2), 75-90.
CR  - Özparlak, A., Karakaya, D., Önder, A., and Günbayı, İ. (2023). Problematic internet use in adolescents: A phenomenological study. Journal of psychiatric and mental health nursing, 30(4), 731–742. https://doi.org/10.1111/jpm.12900
CR  - Poorolajal, J., Ahmadpoor, J., Mohammadi, Y., Soltanian, A. R., Asghari, S. Z., and Mazloumi, E. (2019). Prevalence of problematic internet use disorder and associated risk factors and complications among Iranian university students: a national survey. Health Promotion Perspectives, 9(3), 207.
CR  - Rollo, S., Venuleo, C., Ferrante, L., and De Luca Picione, R. (2023). What Adolescents Have to Say about Problematic Internet Use: A Qualitative Study Based on Focus Groups. International Jurnal of Environmental Research and Public Health, 20(21), 7013. https://doi.org/10.3390/ijerph20217013
CR  - Sanchez Vega, E., Cantero-Téllez, R., and Rodríguez-Martínez, M. del C. (2016). Internet addiction in future teachers. A case study with university students. Innoeduca. International Journal of Technology and Educational Innovation, 2(1), 46–53. https://doi.org/10.20548/innoeduca.2016.v2i1.1063
CR  - Saquib, J., Saquib, N., Carras, M. C., AlSalhi, A., Labrique, A. B., Rajab, A. M., Al Jundi, S., and Almazrou, A. (2022). Individual-level correlates of problematic internet use among adolescents: A nationally representative study in Saudi Arabia. Psychiatry Research Communications, 2(4). https://doi.org/10.1016/j.psycom.2022.100078
CR  - Sunday, O. J., Adesope, O. O., and Maarhuis, P. L. (2021). The effects of smartphone addiction on learning: A meta-analysis. Computers in Human Behavior Reports, 4. https://doi.org/10.1016/j.chbr.2021.100114
CR  - Tekinarslan, E., and Gürer, M. D. (2011). Problematic Internet use among Turkish university students: A multidimensional investigation based on demographics and Internet activities. International Journal of Human Sciences, 8(1), 1028-1051.
CR  - Vigna-Taglianti, F., Brambilla, R., Priotto, B., Angelino, R., Cuomo, G., and Diecidue, R. (2017). Problematic internet use among high school students: Prevalence, associated factors and gender differences. Psychiatry Research, 257, 163-171. https://doi.org/10.1016/j.psychres.2017.07.039
CR  - Witten, I. H., Frank, E., and Hall, M. A. (2016). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann.
CR  - Zhang, X., Chen, H., and Wang, L. (2020). Apriori algorithm-based analysis of university students' learning behaviors and academic performance. Educational Data Mining and Learning Analytics, 14(3), 85-98.
UR  - https://doi.org/10.31466/kfbd.1537843
L1  - https://dergipark.org.tr/tr/download/article-file/4165568
ER  -