Enhancing Computer Students’ Academic Performance Through Explanatory Modeling

  • Leah Mutanu
  • Philip MachokaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1136)


A key challenge facing nowadays universities is the growing attrition rate of computer studies students, attributed to poor academic performance. While extensive research has been conducted on how to enhance students’ performance in computer programming, fewer research investigates other computer courses, especially in sub-Saharan Africa. This paper addresses this gap by describing experiments that revealed some of the factors that influence a student’s overall academic performance at university through explanatory modeling. Our results showed that students’ background in mathematics and their performance in the Introduction to Information Systems course were key in determining performance. Unexpectedly, prior computer skills or secondary school grades had less impact. The strategies identified for enhancing students’ performance include an emphasis on building students’ mathematics background, providing a stringent teaching approach to foundational computing courses, re-structuring of courses in the computer program, and linking courses across the curriculum. Thus, explanatory modeling creates an opportunity to adopt a proactive approach to enhancing the performance of computer studies students.


Computer science higher education Academic performance Explanatory modeling 


  1. 1.
    Akinola, O.S., Nosiru, K.A.: Factors influencing students’ performance in computer programming: a fuzzy set operations approach. Int. J. Adv. Eng. Technol. 7(4), 1141–1149 (2014)Google Scholar
  2. 2.
    Al Murtadha, Y.M., Alhawiti, K.M., Elfaki, A.O., Abdalla, O.A.: Factors influencing academic achievement of undergraduate computing students. Int. J. Comput. Appl. 146(3), 23–28 (2016)Google Scholar
  3. 3.
    Azcona, D., Smeaton, A.F.: Targeting at-risk students using engagement and effort predictors in an introductory computer programming course. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 361–366. Springer, Cham (2017). Scholar
  4. 4.
    Barlow-Jones, G., van der Westhuizen, D.: Pre-entry attributes thought to influence the performance of students in computer programming. In: Liebenberg, J., Gruner, S. (eds.) SACLA 2017. CCIS, vol. 730, pp. 217–226. Springer, Cham (2017). Scholar
  5. 5.
    Chikumba, P.A.: Student performance in computer studies in secondary schools in Malawi. In: Popescu-Zeletin, R., Rai, I.A., Jonas, K., Villafiorita, A. (eds.) AFRICOMM 2010. LNICST, vol. 64, pp. 113–121. Springer, Heidelberg (2011). Scholar
  6. 6.
    Garcia, R.A., Al-Safadi, L.A.: Comprehensive assessment on factors affecting students’ performance in basic computer programming course towards the improvement of teaching techniques. Int. J. Infonomics 6, 682–691 (2013)CrossRefGoogle Scholar
  7. 7.
    Gárcia-Mateos, G., Fernández-Alemán, J.L.: A course on algorithms and data structures using on-line judging. ACM SIGCSE Bull. 41(3), 45–49 (2009)CrossRefGoogle Scholar
  8. 8.
    Giannakos, M.N., Pappas, I.O., Jaccheri, L., Sampson, D.G.: Understanding student retention in computer science education: the role of environment, gains, barriers, and usefulness. Educ. Inf. Technol. 22(5), 2365–2382 (2017)CrossRefGoogle Scholar
  9. 9.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  10. 10.
    Kim, H., Song, H.Y.: Formulating human mobility model in a form of continuous time Markov chain. Proc. Comput. Sci. 10, 389–396 (2012) CrossRefGoogle Scholar
  11. 11.
    Khomokhoana, P.J., Nel, L.: Decoding source code comprehension: bottlenecks experienced by senior computer science students. In: Tait, B., et al. (eds.) SACLA 2019. CCIS, vol. 1136, pp. 17–32. Springer, Cham (2019)Google Scholar
  12. 12.
    Kim, T.K.: T-test as a parametric statistic. Korean J. Anesth. 68(6), 540 (2015)CrossRefGoogle Scholar
  13. 13.
    Kumar, A.N.: Need to consider variations within demographic groups when evaluating educational interventions. ACM SIGCSE Bull. 41(3), 176–180 (2009)CrossRefGoogle Scholar
  14. 14.
    Kremelberg, D.: Practical Statistics: A Quick and Easy Guide to IBM SPSS Statistics, STATA, and other Statistical Software. SAGE, Thousand Oaks (2010)Google Scholar
  15. 15.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  16. 16.
    Namuye, S., Platz, M., Okanda, P., Mutanu, L.: Leveraging health through early warning systems using mobile and service-oriented technology. In: Proceedings of IEEE IST-Africa Conference, pp. 1–10 (2015)Google Scholar
  17. 17.
    Nash, J.: Computer skills of first-year students at a South African University. In: Proceedings of SACLA 2009 Annual Conference of the Southern African Computer Lecturers’ Association, pp. 88–92 (2009)Google Scholar
  18. 18.
    Njoroge, M.M., Wang’eri, T., Gichure, C.: Examination repeats, semester deferments and dropping out as contributors of attrition rates in private universities in Nairobi County Kenya. Int. J. Educ. Res. 4(3), 225–237 (2016)Google Scholar
  19. 19.
    Odhiambo, G.O.: Higher education quality in Kenya: a critical reflection of key challenges. Qual. High. Educ. 17(3), 299–315 (2011)CrossRefGoogle Scholar
  20. 20.
    Pretorius, H.W., Hattingh, M.J.: Factors influencing poor performance in systems analysis and design: student reflections. In: Liebenberg, J., Gruner, S. (eds.) SACLA 2017. CCIS, vol. 730, pp. 251–264. Springer, Cham (2017). Scholar
  21. 21.
    R Core Team: R: a Language and Environment for Statistical Computing (2013).
  22. 22.
    Rauchas, S., Rosman, B., Konidaris, G., Sanders, I.: Language performance at high school and success in first year computer science. ACM SIGCSE Bull. 38(1), 398–402 (2006)CrossRefGoogle Scholar
  23. 23.
    Sedgwick, P.: Pearson’s correlation coefficient. BMJ 345, e4483–e4483 (2012)CrossRefGoogle Scholar
  24. 24.
    Wang, X.M., Hwang, G.J., Liang, Z.Y., Wang, H.Y.: Enhancing students’ computer programming performances, critical thinking awareness and attitudes towards programming: an online peer-assessment attempt. J. Educ. Technol. Soc. 20(4), 58–68 (2017)Google Scholar
  25. 25.
    Wilson, B.C.: A study of factors promoting success in computer science including gender differences. Comput. Sci. Educ. 12(1/2), 141–164 (2002)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Information SystemsUnited States International University AfricaNairobiKenya

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