Makine öğrenmesi kullanarak üniversite not ortalamasını tahmin etme: temel akademik performans faktörlerinin analizi

Abstract

NetHealth projesi, 2015- 2019 yılları arasında, Notre Dame Üniversitesi öğrencilerinin anket ve sağlık ölçümleri baz alınarak yapılmıştır. Projeye iki yıllık süreçte 722, ilave iki yıl için 300 öğrenci daha dahil edilmiştir. Bu çalışmanın amacı, NetHealth proje verileri kullanılarak, öğrencilerin genel not ortalamasını (GNO) etkileyen faktörleri bulmak olarak belirlenmiştir. Veri temizleme adımı sonrasında 265 öğrenci seçilmiştir. Verilerin görselleştirilmesi ve yorumlanması amacıyla, karar ağacı, destek vektör makineleri, lojistik regresyon, çapraz doğrulama ve tabakalı çapraz doğrulama gibi makine öğrenimi metotları kullanılmıştır. Potansiyel faktörler, GNO üzerindeki etkileri analiz edilmek üzere farklı kümelerde kategorize edilerek seçilmiştir. GPA, akademik başarıyı ölçmek için kullanılan kategorik bir değişken olarak tanımlanmıştır. Bunun sonucunda, en yüksek doğruluk oranına karar ağacı metodu kullanılarak ikinci altkümede, %74.50 ile ulaşılmıştır. Bununla beraber, bulguları desteklemek amacıyla, çapraz doğrulama metodu uygulanmıştır. Çapraz doğrulamada ikinci altkümenin doğruluğunun beklenmedik şekilde düşmesi, %72.52 ile ikinci en yüksek doğruluğa sahip birinci altkümeye odaklanma gerekliliğini ortaya çıkarmaktadır. Çalışmanın sonucunda, bu iki metot baz alınarak en yüksek doğruluk oranına birinci alt kümede ulaşılmıştır. Bu kümenin içindeki; internet, mail ve harici kullanımlar, tütün kullanımı, lise türü, ebeveyn geliri, mutluluk seviyesi, özel diyet durumu, iş birliğine yatkınlık ve tembellik eğilimi faktörlerinin GNO üzerinde anlamlı olduğu tespit edilmiştir.

Keywords

• Akademik performans
• Sınıflandırma
• Makine öğrenimi
• Karar ağacı
• Lojistik regresyon
• Destek vektör makinesi

Predicting college GPA using machine learning: an analysis of key academic performance factors

Abstract

The NetHealth project, uses data collected from University of Notre Dame students through surveys and health measurements between 2015 and 2019.722 students were enrolled in two-year-long study, and another 300 students for an additional two years. The aim of the study is to identify influential factors on the student’s Grade Point Average (GPA) by using the NetHealth project survey outputs. After the data-cleaning process, a set of 265 students took place in the study. Machine learning is applied to visualize and interpret the outputs such as decision trees, support vector machines, logistic regression, cross-validation, and stratified cross-validation. Potential factors are selected to analyze their effect on GPA by categorizing in multiple subsets. GPA is defined as categorical variable used as a measure of academic success. As a result, the highest accuracy is achieved using the decision tree algorithm with subset 2 by accuracy of 74.50%. However, to support the findings, cross validation method is implemented. The unexpected result of decreased accuracy in cross validation implication on subset 2, it brings about the necessity of focusing on the second highest accuracy level which is addressed in subset 1 with 72.52%. As a conclusion, the subset 1 is performed best among two high resulted methods with the factors, use of internet, email, other leisure, used tobacco, high school type, parent income, happy, special type of diet, likes to cooperate with others, and tends to be lazy.

Keywords

• Academic performance
• Classification
• Machine learning
• Logistic regression
• Support vector machine

References

1. IBM (2020) Machine Learning, IBM Cloud Learn Hub. https://www.scirp.org/reference/referencespapers?referenceid=3554242 Accessed: 05.05.2026.
2. Hartmann, M.E. and Prichard, J.R. (2018) Calculating the contribution of sleep problems to undergraduates’ academic success, Sleep Health, 4(5): 463-471.
3. Vedaa et al. (2019) Insomnia, sleep duration and academic performance: A national survey of Norwegian college and university students, Sleep Medicine, 64.
4. Yassin, A. et al. (2020) Prevalence of sleep disorders among medical students and their association with poor academic performance: A cross-sectional study, Annals of Medicine and Surgery, 58: 124-129.
5. Okano, K. et al. (2019) Sleep quality, duration, and consistency are associated with better academic performance in college students, npj Science of Learning, 4(1).
6. Phillips, A.J. et al. (2017) Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing, Scientific Reports, 7(1).
7. Raley, H. et al. (2016) The impact of duration of sleep on academic performance in university students, Madridge Journal of Nursing, 1(1): 11-18.
8. Zhu, H. (2025) The relationship between sleep quality, sleep duration and students’ academic performance, Proceedings of the 2nd International Conference on Applied Psychology and Marketing Management, 153-158.

9. Hayek, J. et al. (2021) Increased adherence to the Mediterranean diet and higher efficacy beliefs are associated with better academic achievement: A longitudinal study of high school adolescents in Lebanon, International Journal of Environmental Research and Public Health, 18(13): 6928.
10. Burrows, T. et al. (2017) Associations between dietary intake and academic achievement in college students: A systematic review, Healthcare, 5(4): 60.
11. Faught, E.L. et al. (2019) The longitudinal impact of diet, physical activity, sleep, and screen time on Canadian adolescents’ academic achievement: An analysis from the COMPASS study, Preventive Medicine, 125: 24-31.
12. Reuter, P., Forster, B. and Brister, S. (2019) The influence of eating habits on the academic performance of university students, Preprint.
13. Kim, S.Y. et al. (2016) Dietary habits are associated with school performance in adolescents, Medicine, 95(12).
14. Sinag, J.M. (2026) Exploring the influence of nutrition on academic success among first-year bachelor of physical education students at Bataan Peninsula State University-Orani Campus, The Physical Educator, 83(1).
15. Al-Hebaish, S.M. (2012) The correlation between general self-confidence and academic achievement in the oral presentation course, Theory and Practice in Language Studies, 2(1).
16. Telbis, N.M., Helgeson, L. and Kingsbury, C. (2014) International Students’ Confidence and Academic Success, Journal of International Students, 4(4): 330-341.
17. Meisha, D.E. and Al-Dabbagh, R.A. (2021) Self-confidence as a predictor of senior dental student academic success, Journal of Dental Education.
18. Rosli, Y., Othman, H., Ishak, I., Lubis, S.H., Saat, N.Z.M. and Omar, B. (2012) Self-esteem and Academic Performance Relationship Amongst the Second Year Undergraduate Students of Universiti Kebangsaan Malaysia, Kuala Lumpur Campus, Procedia - Social and Behavioral Sciences, 60: 582-589.
19. Chesser-Smyth, P.A. and Long, T. (2012) Understanding the influences on self-confidence among first-year undergraduate nursing students in Ireland, Journal of Advanced Nursing, 69(1): 145-157.
20. Meral, M., Colak, E. and Zereyak, E. (2012) The Relationship between Self-Efficacy and Academic Performance, Procedia - Social and Behavioral Sciences, 46: 1143-1146.
21. Tridinanti, G. (2018) The Correlation between Speaking Anxiety, Self-Confidence, and Speaking Achievement of Undergraduate EFL Students of Private University in Palembang, International Journal of Education and Literacy Studies, 6(4): 35.
22. Zajacova, A., Lynch, S.M. and Espenshade, T.J. (2005) Self-Efficacy, Stress, and Academic Success in College, Research in Higher Education, 46(6): 677-706.
23. Web Page: 10 Things Teachers Should Never Say or Do, ThoughtCo. https://www.thoughtco.com/what-teachers-should-never-say-or-do-4088818
24. Web Page: Rob Eastaway, https://robeastaway.com/blog/lazy-revision Last access date: 05.05.2026.
25. Latif, E. (2011) The impact of retirement on psychological well-being in Canada, The Journal of Socio-Economics, 40(4): 373-380.
26. Gonçalves, S. (2011) Intangible culture, cooperation and intercultural dialogue among university students, Intercultural Education, 22(1): 83-95.
27. Web Page: Team Work Makes the Dream Work, CollegeMarker Blog. https://collegemarker.com/blogs/team-work-makes-the-dream-work
28. Web Page: Four Benefits of Teamwork for Student Development & Success, Marlborough University. https://www.marlborough.org/news/~board/stem/post/four-benefits-of-teamwork-for-student-development-success
29. Web Page: Making Teamwork Work, S. Reifenberg. https://www.insidehighered.com/advice/2021/04/07/how-help-students-work-more-effectively-teams-opinion
30. Sulaiman, M., Mat, Z., Husain, F.C., Sulaiman, A., Nizah, M.A.M. and Latiff, L.A. (2017) The impact of teamwork skills on students in Malaysian public universities, The Social Sciences, 12(9): 1682-1686.
31. Web Page: Understanding the Team, York University. https://learningcommons.yorku.ca/groupwork/understanding-the-team/
32. Decker, S., Greenwood, A., Kirby, S. and Moore, D. (1999) Taking Children Seriously: Applications of Counselling and Therapy in Education, London: Bloomsbury Publishing.
33. Tsai, K.C. (2012) Play, Imagination, and Creativity: A Brief Literature Review, Journal of Education and Learning, 1(2).
34. Lin, W.S., Hsu, Y. and Liang, C. (2014) The mediator effects of conceiving imagination on academic performance of design students, International Journal of Technology and Design Education, 24(1): 73-89.
35. Klottrup, A. and Egan, K. (1992) Imagination in Teaching and Learning: Ages 8-15, British Journal of Educational Studies, 40(4): 415.
36. Tyng, C.M., Amin, H.U., Saad, M.N. and Malik, A.S. (2017) The influences of emotion on learning and memory, Frontiers in Psychology, 8.
37. Mahakud, G.C. and Yadav, R. (2015) Effects of Happiness on Mental Health, International Journal of Indian Psychology, 2(3).
38. Tabbodi, M., Rahgozar, H. and Makki Abadi, M.M. (2015) The relationship between happiness and academic achievements, European Online Journal of Natural and Social Sciences: Proceedings, 4(1): 241.
39. Khan, T.M., Mansoor, S., Kaleem, M., Zafar, M.S., Shoail, A., Nauman, S. et al. (2020) Evaluation of impact of happiness on academic performance among medical students of Rawalpindi Medical University, Pakistan, European Journal of Medical and Health Sciences, 2(6).
40. Shoaakazemi, M., Momeni Javid, M., Keramati, R. and Ebrahimi Tazekand, F. (2013) The relationship between happiness, meta-cognitive skills (self-regulation, problem-solving) and academic achievement of students in Tehran, Life Science Journal, 10(4s): 452-457.
41. Quinn, P.D. and Duckworth, A.L. (2007) Happiness and academic achievement: Evidence for reciprocal causality, Annual Meeting of the American Psychological Society, May.
42. Otaghi, M., Sayehmiri, K., Valizadeh, R. and Tavan, H. (2019) Correlation Between Happiness and Academic Achievement in Iranian Students: A Meta-Analysis Letter, Shiraz E-Medical Journal, In Press.
43. Lumontod, R.Z. III (2019) The role of goal concordance on happiness and college students’ academic performance, International Journal of Research Studies in Psychology, 8(1).
44. Web Page: The Importance of Emotional Stability and How to Look for It When Hiring, People Dynamics, Inc. https://peopledynamics.co/importance-emotional-stability Last access date: 06.05.2026.
45. Kalita, S. (2016) Academic achievement of high school students in relation to their emotional stability, Journal of Process Management and New Technologies, 4(4).
46. Stajkovic, A.D., Bandura, A., Locke, E.A., Lee, D. and Sergent, K. (2018) Test of three conceptual models of influence of the big five personality traits and self-efficacy on academic performance: A meta-analytic path-analysis, Personality and Individual Differences, 120: 238-245.
47. Edjah, K., Ankomah, F., Domey, E. and Laryea, J.E. (2020) Stress and Its Impact on Academic and Social Life of Undergraduate University Students in Ghana: A Structural Equation Modeling Approach, Open Education Studies, 2(1): 37-44.
48. Web Page: How Stress Affects College Students’ Academic Performance, Owlcation. https://owlcation.com/academia/College-Life-The-Effects-of-Stress-on-Academic-Performance
49. Terada, Y. (2018) The science behind student stress, Edutopia, (8).
50. Scott, E.A. (2013) 8 Keys to Stress Management (8 Keys to Mental Health), New York: W. W. Norton & Company.
51. Stallman, H.M. and Hurst, C.P. (2016) The University Stress Scale: Measuring Domains and Extent of Stress in University Students, Australian Psychologist, 51(2): 128-134.
52. Bayram, N. and Bilgel, N. (2008) The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students, Social Psychiatry and Psychiatric Epidemiology, 43(8): 667-672.
53. Madhavika, W.D.N. and Kodithuwakku, K.C. (2019) Internet Usage and Academic Performance of Sri Lankan Undergraduates, International Journal of Academic Research in Business and Social Sciences, 9(7).
54. Haarms, R. (2015) Utilizing Personal Internet Usage as a Predictor for Attention and Academic Success among Filipino University Students.
55. Li, J. and Ding, W. (2026) The moderating role of time management in the relationship between social media addiction and academic and social outcomes among university students, Journal of the Chinese Institute of Engineers, 1-23.
56. Xu, X., Wang, J., Peng, H. and Wu, R. (2019) Prediction of academic performance associated with internet usage behaviors using machine learning algorithms, Computers in Human Behavior, 98: 166-173.
57. Prieto-Latorre, C. et al. (2022) The ideal use of the internet and academic success: Finding a balance between competences and knowledge using interval multiobjective programming, Socio-Economic Planning Sciences, 81: 101208.
58. Fatema, K. et al. (2020) Impact of using the internet on students: A sociological analysis at Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh, Open Journal of Social Sciences, 8(12): 71-83.
59. Kim, S. (2011) The effects of internet use on academic achievement and behavioral adjustment among South Korean adolescents: Mediating and moderating roles of parental factors, PhD Thesis, Syracuse University.
60. Prieto-Latorre, C. et al. (2022) The ideal use of the internet and academic success: Finding a balance between competences and knowledge using interval multiobjective programming, Socio-Economic Planning Sciences, 81: 101208.
61. Cannonier, C., Burke, M.G. and Steward, K. (2018) Smoking, health and academic outcomes: Evidence from a limited smoking campus policy, Health Economics, Policy and Law, 14(2): 205-230.
62. Hernández-Serrano, O., Gras, M. and Font-Mayolas, S. (2018) Concurrent and simultaneous use of cannabis and tobacco and its relationship with academic achievement amongst university students, Behavioral Sciences, 8(3): 31.
63. Meressa, K., Mossie, A. and Gelaw, Y. (2009) Effect of Substance Use on Academic Achievement of Health Officer and Medical Students of Jimma University, Southwest Ethiopia, Ethiopian Journal of Health Sciences, 19(3).
64. Mekonen, T. et al. (2017) Substance use as a strong predictor of poor academic achievement among university students, Psychiatry Journal, 2017: 1-9.
65. Arria, A.M. et al. (2015) The academic consequences of marijuana use during college, Psychology of Addictive Behaviors, 29(3): 564-575.
66. Bolin, R.M., Pate, M. and McClintock, J. (2017) The impact of alcohol and marijuana use on academic achievement among college students, The Social Science Journal, 54(4): 430-437.
67. Suerken, C. et al. (2016) Marijuana use trajectories and academic outcomes among college students, Drug and Alcohol Dependence, 162: 137-145.
68. Souza, J., Hamilton, H. and Wright, M. (2019) Academic performance and consumption of alcohol, marijuana, and cocaine among undergraduate students from Ribeirão Preto-Brazil, Texto & Contexto - Enfermagem, 28(spe).
69. Brook, J.S. et al. (2008) The association between earlier marijuana use and subsequent academic achievement and health problems: A longitudinal study, The American Journal on Addictions, 17(2): 155-160.
70. Ominowa, O.T. and Akanbi, G.A. (2026) Comparative assessment between substance abuse on academic performance and deviant behavior among secondary school students in Akure South Local Government Area of Ondo State, Nigeria, Federal University Gusau Faculty of Education Journal, 6(2): 268-273.
71. Dil, S. and Bulantekin Düzalan, Ö. (2011) Determination of the relationship between family functionality and locus of control and levels of academic success among nursing students.
72. Arslantaş, İ., Özkan, M. and Külekçi, E. (2012) The analysis of academic achievement for some demographic variables in education faculty students.
73. Tavani, C.M. and Losh, S.C. (2003) Motivation, self-confidence, and expectations as predictors of the academic performance among high school students, Child Study Journal, 33(3): 141-152.
74. Merritt, D.L. and Buboltz, W. (2015) Academic Success in College: Socioeconomic Status and Parental Influence as Predictors of Outcome, Open Journal of Social Sciences, 3(5): 127-135.
75. Azhar, M., Nadeem, S., Naz, F., Perveen, F. and Sameen, A. (2014) Impact of parental education and socio-economic status on academic achievements of university students, European Journal of Psychological Research, 1(1).
76. Cilasun, S. (2013) An analysis of academic performance: could family income and medium of instruction be determinants?, Sosyoekonomi, 19(19).
77. Web Page: Influence of parental socioeconomic status on students’ academic performance in senior secondary schools in Rivers State, BW Academic Journal.
78. Abdu-Raheem, B.O. (2015) Parents' socio-economic status as predictor of secondary school students' academic performance in Ekiti State, Nigeria, Journal of Education and Practice, 6(1): 123-128.
79. Milne, A.M. et al. (1986) Single parents, working mothers, and the educational achievement of school children, Sociology of Education, 59(3): 125.
80. Abubakar, R.B. and Oguguo, O.D. (2011) Age and gender as predictors of academic achievement of college mathematics and science students, Journal of Educational and Social Research, 1(2): 89-93.
81. Tessema, M., Ready, K. and Malone, C. (2012) Effect of gender on college students' satisfaction and achievement: The case of a midsized Midwestern public university, International Journal of Business and Social Science, 3(10).
82. Chee, K.H., Pino, N.W. and Smith, W.L. (2005) Gender differences in the academic ethic and academic achievement, College Student Journal, 39(3): 604-619.
83. Chukwuma-Nwankwo, I.F., Njoku, N.C., Okonkwo, A.F. and Nwafor, C.R. (2026) Gender and instructional strategy effects on English speaking achievement among senior secondary school students in Ebonyi State, Nigeria, UNIZIK Journal of Educational Research and Policy Studies, 21(1): 299-236.
84. Neave, G., Teichler, U., van Vught, F.A., Huber, L., Klose-Berger, A. and Möhle, H. (1989) Research on higher education in Europe, European Journal of Education, 24(3).
85. Roy, H.J. (1999) Studying abroad as a form of experiential education, Liberal Education, 36-41.
86. Pietro, G.D. and Page, L. (2008) Who studies abroad? evidence from France and Italy, European Journal of Education, 43(3): 389-398.
87. Llanes, A. (2010) The many faces of study abroad: An update on L2 gains during study abroad, International Journal of Multilingualism. https://eric.ed.gov/?id=EJ933948 (Accessed: 06.05.2026).
88. Birch, E.R. and Miller, P.W. (2007) The influence of type of high school attended on university performance, Australian Economic Papers, 46(1): 1-17.
89. Evans, M. and Farley, A. (1998) Institutional characteristics and the relationship between students’ first-year university and final-year secondary school academic performance, Monash University.
90. Cansız, M., Özbaylanlı, B. and Mustafa, H. (2019) Impact of school type on student academic achievement, Education and Science, 44.
91. Banai, B. and Perin, V. (2016) Type of high school predicts academic performance at university better than individual differences, PLOS ONE, 11(10): e0163996.
92. Graunke, S.S. and Woosley, S.A. (2005) An exploration of the factors that affect the academic success of college sophomores, College Student Journal, 39(2): 367.
93. Wenz, M. and Yu, W.-C. (2010) Term-time employment and the academic performance of undergraduates, Journal of Education Finance, 35(4): 358-373.
94. Hawkins, C.A., Smith, M.L., Hawkins, R.C. and Grant, D. (2005) The relationships among hours employed, perceived work interference, and grades as reported by undergraduate social work students, Journal of Social Work Education, 41(1): 13-27.
95. Hämäläinen, W. and Vinni, M. (2006) Comparison of machine learning methods for intelligent tutoring systems, International Conference on Intelligent Tutoring Systems, 525-534.
96. Al Mayahi, K. and Al-Bahri, M. (2020) Machine learning based predicting student academic success, ICUMT 2020, 264-268.
97. Ertekin, S., Huang, J., Bottou, L. and Giles, L. (2007) Learning on the border: active learning in imbalanced data classification, ACM Conference on Information and Knowledge Management, 127-136.
98. Brownlee, J. (2017) Why is imbalanced classification difficult?
99. IBM Cloud Education (2021) What is overfitting?
100. Lyashenko, V. and Jha, A. (2022) Cross-validation in machine learning: how to do it right.
101. Scikit-learn (2022) Cross-validation: evaluating estimator performance.
102. Thammasiri, D., Delen, D., Meesad, P. and Kasap, N. (2014) A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition, Expert Systems with Applications, 41(2): 321-330.
103. Lendave, V. (2021) Performance measure of stratified K-fold cross-validation.
104. Paliwal, M. and Kumar, U.A. (2009) Neural networks and statistical techniques: A review of applications, Expert Systems with Applications, 36(1): 2-17.
105. Jaiswal, S. (2011) Decision tree classification algorithm.
106. Gupta, A. (2020) Difference between Gini impurity and entropy in decision tree.
107. MathWorks (2022) Support Vector Machine (SVM).
108. NetHealth Data (2022) NetHealth Data.
109. Zhu, X., Wang, X. and Ok, Y.S. (2019) The application of machine learning methods for prediction of metal sorption onto biochars, Journal of Hazardous Materials, 378: 120727.
110. Jebli, I., Belouadha, F.Z., Kabbaj, M.I. and Tilioua, A. (2021) Prediction of solar energy guided by Pearson correlation using machine learning, Energy, 224: 120109.
111. Güner, G. (2020) Machine learning: correlation matrix, feature selection, class imbalance, decision trees, precision/recall/F1 score.
112. Pedregosa, F. et al. (2011) Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 2825-2830.
113. Natek, S. and Zwilling, M. (2014) Student data mining solution–knowledge management system related to higher education institutions, Expert Systems with Applications, 41(14): 6400-6407.