The objective of this article is to measure through intelligent techniques, the quality of university life in a university population. In this investigation, a dependent variable called quality of university life is taken, as well as 10 independent variables: Academic load, economic resources, relationship with classmates, relationship with professors, curriculum, extracurricular activities, current housing, family relationships, emotional state and university environment. In the samplings of these variables, 127 surveys were carried out on university students of a public university located in the central region of Colombia. Subsequently, the most relevant variables were selected throughout statistical techniques, in order to establish a file to be analyzed through the decision tree classification algorithm J48from the Weka platform. The results show, with over an 80 % effectiveness, that the most influential variables in the quality of life of a university student are: University environment, current housing, emotional state, and relationships with professors. Finding a lot of times that the quality of university life can also depend of external variables to the university such as: Current housing and emotional state. These results are of great importance in the design of new university policies.


  1. ONU, (2012). “ONU examina relación entre felicidad y desarrollo”.Available in:
  2. Datu, J., King, R., and Valdez, J. “The Academic Rewards of Socially-Oriented Happiness: Interdependent Happiness Promotes Academic Engagement”. Journal of School Psychology, vol. 61, pp. 19–31, 2017.
  3. Ramírez, P., Fuentes, C. (2013). “Felicidad y Rendimiento Académico: Efecto Moderador de la Felicidad sobre Indicadores de Selección y Rendimiento Académico de Alumnos de Ingeniería Comercial”. Formación Universitaria, vol. 6, no. 3, pp. 21–30. doi:
  4. Habibzadeh, S. and Allahvirdiyani, K. (2011). “Effects of economic and non economic factors on happiness on primary school teachers and Urmia University professors”.vol. 30, pp. 2050–2051. doi:
  5. Kamthan, S., et al. (2019). “Happiness among second year MBBS students and its correlates using Oxford Happiness Questionnaire”, Journal of Oral Biology and Craniofacial Research, vol. 9, pp. 190–192. doi:
  6. Talebzadeh, F. and Samkan M. (2011). “Happiness for our kids in schools: A conceptual model”, Procedia - Social and Behavioral Sciences, vol. 29, pp. 1462–1471. doi:
  7. Chan G., Miller, P. and Tcha, M. (2005). “Happiness In University Education”. International Review of Economics Education, vol. 4, pp. 20–45. doi:
  8. Vigneswaran Applasamy, V., et al. (2014). “Measuring Happiness in Academic Environment:
  9. A Case Study of the School Of Engineering at Taylor’s University (Malaysia)”. Procedia -
  10. Social and Behavioral Sciences, vol. 123, pp. 106–112. doi: 10.1016/j.sbspro.2014.01.1403.
  11. Aziz. R., et al. (2014). “Personality and happiness among academicians in Malaysia”. Procedia- Social and Behavioral Sciences, vol. 116, pp. 4209–4212. doi:
  12. Öztürk A. and Mutlu, T. (2010). “The relationship between attachment style, subjective well-being, happiness and social anxiety among university students’”. Procedia Social and Behavioral Sciences. vol. 9, pp. 1772–1776. doi:
  13. Moeinaddini, M., et al. (2020). “Proposing a New Score to Measure Personal Happiness by Identifying the Contributing Factors”. Measurement, vol. 151, 107-115.doi:
  14. Mangaraj, B.K. and Aparajita, U. (2020). “Constructing a generalized model of the human development index”. Socio-Economic Planning Sciencesvol. 70, 100778. doi:
  15. Karagiannis, R. and Karagiannis, G. (2020). “Constructing composite indicators with Shannon entropy: The case of Human Development Index”. Socio-Economic Planning Sciences, vol.70, 100701. doi:
  16. Pinar M., Stengos, T. and Topaloglou, N. (2017). “Testing for the implicit weights of the dimensions of the Human Development Index using stochastic dominance”. Economics Letters. vol. 161, pp. 38–42. doi:
  17. Chen l., Cai, W., and Ma, M. (2020) “Decoupling or delusion? Mapping carbon emission per capita based on the human development index in Southwest China”. Science of The Total Environment. vol. 741, 138722. doi:
  18. Riahi M., et al. (2018). “Diarrhea deaths in children among countries with different levels of the human development index”. Data in Brief, vol. 17, pp. 954–960. doi:
  19. Sarkodie, S.A., and Adams, S. (2020). “Electricity access, human development index, governance and income inequality in Sub-Saharan Africa”. Energy Reports, vol. 6, pp. 455–466.doi:
  20. Martínez-Mesa J.,et al. (2017). “Exploring disparities in incidence and mortality rates of breast and gynecologic cancers according to the Human Development Index in the Pan-American region”. Public Health, vol. 149, pp. 81–88. doi:
  21. Veisani, Y., et al. (2018). “Global incidence and mortality rates in pancreatic cancer and the association with the Human Development Index: decomposition approach”. Public Health. vol. 156, pp. 87–91. doi:
  22. Hwang, Y-H, and Hsiao, C-K, Lin, P-W. (2019). “Globally temporal transitions of blood lead levels of preschool children across countries of different categories of Human Development Index”. Science of the Total Environment, vol. 659, pp. 1395–1402. doi:
  23. Wang, Z., et al. (2018). “Renewable energy consumption, economic growth and human development index in Pakistan: Evidence form simultaneous equation model”. Journal of Cleaner Production, vol. 184, pp. 1081–1090.doi:
  24. Martínez-Guido, S., González-Campos, J., and Ponce-Ortega, J. (2019). “Strategic planning to improve the Human Development Index in disenfranchised communities through satisfying food, water and energy needs”. Food and Bioproducts Processing, vol. 117, pp. 14–29. doi:
  25. Khazaei, S., et al. (2017). “Suicide rate in relation to the Human Development Index and other health related factors: A global ecological study from 91 countries”. Journal of Epidemiology and Global Health, vol. 7, pp. 131-134. doi:
  26. Long, X., et al. (2020). “Sustainability evaluation based on the Three-dimensional Ecological Footprint and Human Development Index: A case study on the four island regions in China”. Journal of Environmental Management, vol. 265, 110509. doi:
  27. Yue, S., Shen, Y., and Yuan, J. (2019). “Sustainable total factor productivity growth for 55 states: An application of the new malmquist index considering ecological footprint and human development index”. Resources, Conservation & Recycling. vol. 146, pp. 475–483. doi:
  28. Hickel, J. (2020). “The sustainable development index: Measuring the ecological efficiency of human development in the anthropocene”. Ecological Economics, vol. 167, 106331. doi:
  29. Biggeri, M. and Mauro, V. (2018). “Towards a more ´Sustainable’ Human Development Index: Integrating the environment and freedom”. Ecological Indicators. vol. 91, pp. 220–231. doi:
  30. Zhang, X. and P. Luo. (2021). “Analysis of psychological education factors based on computer software and hardware collaboration and data mining”. Microprocessors and Microsystems, vol. 81, 103744. doi:
  31. Rong, L. (2021). “Remote case teaching mode based on computer FPGA platform and data mining.” Microprocessors and Microsystems, vol. 83, 103986. doi:
  32. Lemay, D., C. Baek. and T. “Doleck, Comparison of learning analytics and educational data mining: A topic modeling approach”. Computers and Education: Artificial Intelligence, vol. 2, 100016. doi:
  33. Omrani, H., Alizadeh, A. and Amini M. (2020). “A new approach based on BWM and MULTIMOORA methods for calculating semi-human development index: An application for provinces of Iran”. Socio-Economic Planning Sciences. vol. 70, 100689. doi:
  34. “Weka 3: Machine Learning Software in Java”. Available:
  35. Castrillón O., W. Sarache and S. Ruiz. “Predicción del Rendimiento Académico por medio de Técnicas de Inteligencia Artificial”. Formación Universitaria, vol. 13, pp. 93–102, 2020.
  36. Valdivieso, C.E., R. Valdivieso and O.A. Valdivieso. (2011). “Determinación del tamaño muestral mediante el uso de árboles de decisión”. UPB - Investigación & Desarrollo, vol.11, pp. 148–176. doi: 10.23881/idupbo.011.1-4e.
  37. Valencia, M., J. Correa and F. Díaz. (2015). “Métodos estadísticos clásicos y bayesianos para el pronóstico de demanda. Un análisis comparativo”. Revista Facultad de Ciencias Universidad Nacional de Colombia, vol. 4, pp. 52–67. doi:
How to Cite
Ibañez Ramírez, J. S., Echeverri Salazar , T., & Castrillón Gómez, O. D. (2021). PREDICTION OF THE QUALITY OF UNIVERSITY LIFE THROUGH DATA MINING TECHNIQUES. Revista Ingenierías Universidad De Medellín, 21(40), 1-14.


Download data is not yet available.

Send mail to Author

Send Cancel

We are indexed in