Predicting Factors of Students Dissatisfaction for Retention

  • Mohammad Aman Ullah
  • Mohammad Manjur Alam
  • Md. Mahiuddin
  • Mohammed Mahmudur Rahman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)


Education sector became challenging as well as competitive due to the huge availability of institutions worldwide. This increases students drop out at an alarming rate. Most of the dropout is due to dissatisfaction of the students. This paper investigates the reasons of students dissatisfaction from a feedback of a university student using Descriptive Statistics, Logistic Regression Analysis and some data mining techniques such as Naïve Bayes, Logistic Regression and Random Forest and found relationship exists between student dissatisfaction and student retention. Our study also found that, a high level of dissatisfaction of the students is mostly due to the cafeteria services and extra-curricular activities. They are moderately dissatisfied due to quality lecture and Lab Facilities. Our analysis has also shown that, overall, students are satisfied, but mostly they do not want to recommend others for this university. On the basis of the result, we then recommended some issues to be fulfilled for student retention.


Satisfaction Student Retention Algorithms SPSS 


  1. 1.
    Manzoor, H.: Measuring student satisfaction in public and private universities. Pak. Global J. Manag. Bus. Res. Interdiscip. 13(3) (2013)Google Scholar
  2. 2.
    Tessema, M.T., Ready, K., Yu, W.-Y.: Factors affecting college students’ satisfaction with major curriculum: evidence from nine years of data. Int. J. Humanit. Social Sci. 2, 34–44 (2012)Google Scholar
  3. 3.
    Negricea, C.I., Edu, T., Avram, E.M.: Establishing influence of specific academic quality on student satisfaction. In: Procedia—Social and Behavioral Sciences, vol. 116, pp. 4430–4435 (2014)Google Scholar
  4. 4.
    Yunus, N.K.Y., Ishak, S., Razak, A.Z.A.A.: Motivation, empowerment, service quality and polytechnic students’ level of satisfaction in Malaysia. Int. J. Bus. Social Sci. 1, 120–128 (2010)Google Scholar
  5. 5.
    Wei, C.C., Ramalu, S.S.: Students satisfaction towards the university: does service quality matters? Int. J. Educ. 3(2), (2011)Google Scholar
  6. 6.
    Hanaysha, J., Abdullah, H.H., Warokka, A.: Service quality and students’ satisfaction at higher learning institutions: the competing dimensions of Malaysian Universities’ competitiveness. J. Southeast Asian Res. 1, 1–9 (2011)Google Scholar
  7. 7.
    Stokes, S.P.: Satisfaction of college students with the digital learning environment: do learners’ temperaments make a difference? Internet High. Educ. 4, 31–44 (2001)CrossRefGoogle Scholar
  8. 8.
    Macedo-Rouet, M., Ney, M., Charles, S., Lallich-Boidin, G.: Students’ performance and satisfaction with Web vs. paper-based practice quizzes and lecture notes. Comput. Educ. 53(2), 375–384 (2009)CrossRefGoogle Scholar
  9. 9.
    Guo, W.W.: Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction. Expert Syst. Appl. 37(4), 3358–3365 (2010)CrossRefGoogle Scholar
  10. 10.
    Zhang, G. et al.: Identifying factors influencing engineering student graduation and retention: a longitudinal and cross-institutional study (2002)Google Scholar
  11. 11.
    Herzog, S.: Estimating student retention and degree-completion time: decision trees and neural networks vis-à-vis regression. In: New Directions for Institutional Research, p. 17, (2006)Google Scholar
  12. 12.
    Dekker, G. et al.: Predicting Students Drop Out: A Case Study. pp. 41–50 (2009)Google Scholar
  13. 13.
    Osmanbegović, E., Suljić, M.: Data mining approach for predicting student performance. Econ. Rev. 10, 3–12 (2012)Google Scholar
  14. 14.
    Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13, 61–72 (2013)Google Scholar
  15. 15.
    Radha, D., et al.: A novel approach to analyze students’ expectation from colleges using data mining technique. Int. J. Comput. Appl. 137(5), 25–28 (2016)Google Scholar
  16. 16.
    Agaoglu, M.: Predicting instructor performance using data mining techniques in higher education. IEEE Access 4, 2379–2387 (2016)CrossRefGoogle Scholar
  17. 17.
    Alkhasawneh, R. et al.: Modeling student retention in science and engineering disciplines using neural networks. In: 2011 IEEE Global Engineering Education Conference (EDUCON), pp. 660–663 (2011)Google Scholar
  18. 18.
    Kabakchieva, D.: Student performance prediction by using data mining classification algorithms. Int. J. Comput. Sci. Manag. Res. 1, 686–690 (2012)Google Scholar
  19. 19.
    Kovacic, Z.: Predicting student success by mining enrolment data. Res. High. Educ. J. (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mohammad Aman Ullah
    • 1
  • Mohammad Manjur Alam
    • 1
  • Md. Mahiuddin
    • 1
  • Mohammed Mahmudur Rahman
    • 1
  1. 1.International Islamic University ChittagongChittagongBangladesh

Personalised recommendations