WITS 2020 pp 113-123 | Cite as

Predicting Student’s Performance Based on Cloud Computing

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 745)


COVID-19 Coronavirus epidemic has created a calamitous worldwide situation. The Moroccan University Sidi Mohammed Ben Abdellah of Fez mobilized to develop platforms for distance learning. New tools for e-learning (online learning) were developed, and advanced learning management systems (LMSs) were deployed. Predicting student's performance is more difficult because of the large amount of data including the huge number of learners and the educational content variety. Currently, in the University of Fez, the lack of current mechanisms to assess and control the development and performance of the students is not discussed. In this context, Cloud computing is becoming a hot research subject when faced with large-scale data and is commonly used to solve this issue. Big Data is a leading concept because of its permanent optimization and opportunities offering data collection, analysis, storage, optimization, processing, and data representation to e-learning professionals. The objective of this paper is to develop a model of predicting student’s performance based on cloud computing as part of the normal enhancement of online learning by incorporating new information and communication technologies.


Prediction student’s performance Big data Cloud computing Educational data mining E-learning 


  1. 1.
    El Aissaoui O, El Alami El Y, Madani L, Oughdir AD, El Allioui Y (2020) A Multiple linear regression-based approach to predict student performance. In: Ezziyyani M (ed) Advanced intelligent systems for sustainable development (AI2SD’2019), vol 1102. Springer International Publishing, Cham, pp 9–23CrossRefGoogle Scholar
  2. 2.
    Sangeeta K, PanduRanga Vital T (2020) Student classification based on cognitive abilities and predicting learning performances using machine learning models. IJRTE 8(6):3554–3569.
  3. 3.
    Shahiri AM, Husain W, Rashid NA (2015) A review on predicting student’s performance using data mining techniques. Procedia Comput Sci 72:414–422. Scholar
  4. 4.
    Anshari M, Alas Y, Yunus NHM (2016) Online learning: trends, issues, and challenges in the big data era. Online Learn 12(1):14Google Scholar
  5. 5.
    Ashraf A, El-Bakry HM, El-razek SMA, El-Mashad Y, Mastorakis N (2015) Enhancing big data processing in educational systems, p 7Google Scholar
  6. 6.
    El Mhouti A, Erradi M, Nasseh A (2019) Application of cloud computing in e-learning: a basic architecture of cloud-based e-learning systems for higher education. In: Ben Ahmed M, Boudhir AA, Younes A (eds) Innovations in smart cities applications, 2 edn. Springer International Publishing, Cham, pp 319–333Google Scholar
  7. 7.
    Hartshorne R, Ajjan H (2009) Examining student decisions to adopt Web 2.0 technologies: theory and empirical tests. J Comput High Educ 21(3):183–198. Scholar
  8. 8.
    Fry K (2001) E‐learning markets and providers: some issues and prospects. Educ Train 43(4/5):233–239.
  9. 9.
    Arkorful V, Abaidoo N (2014) The role of e-learning, the advantages and disadvantages of its adoption in higher education. 2(12):14Google Scholar
  10. 10.
    Abbad MM, Morris D, De Nahlik C (2009) Looking under the bonnet: factors affecting student adoption of e-learning systems in Jordan. IRRODL 10(2).
  11. 11.
    Keller C, Cernerud L (2002) Students’ perceptions of e-learning in university education. J Educ Media 27(1–2):55–67. Scholar
  12. 12.
    Dublin L (2003) If you only look under the street lamps … or nine e-learning myths.
  13. 13.
    Algahtani A  (2011) Evaluating the effectiveness of the e-learning experience in some universities in Saudi Arabia from male students perceptions, p 328Google Scholar
  14. 14.
    Fariba TB (2013) Academic performance of virtual students based on their personality traits, learning styles and psychological well being: a prediction. Procedia Soc Behav Sci 84:112–116. Scholar
  15. 15.
    Quadri MMN Drop out feature of student data for academic performance using decision tree techniques, p 4Google Scholar
  16. 16.
    Romero C, Ventura S, Espejo PG, Hervás C (2008) Data mining algorithms to classify students, p 10Google Scholar
  17. 17.
    Sun S, Huang R (2010) An adaptive k-nearest neighbor algorithm. In: 2010 seventh international conference on fuzzy systems and knowledge discovery, Yantai, China, pp 91–94.
  18. 18.
    Harahap F, Harahap AYN, Ekadiansyah E, Sari RN, Adawiyah R, Harahap CB (2018) Implementation of Naïve Bayes classification method for predicting purchase. In: 2018 6th international conference on cyber and IT service management (CITSM), Parapat, Indonesia, pp 1–5.
  19. 19.
    Irfiani E, Elyana I, Indriyani F, Schaduw FE, Harmoko DD (2018) Predicting grade promotion using decision tree and Naïve Bayes classification algorithms. In: 2018 third international conference on informatics and computing (ICIC), Palembang, Indonesia, pp 1–4.
  20. 20.
    Kaufman LM (2009) Data security in the world of cloud computing. IEEE Secur Privacy Mag 7(4):61–64. Scholar
  21. 21.
    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18. Scholar
  22. 22.
    Liu F et al (2011) NIST cloud computing reference architecture, p 35Google Scholar
  23. 23.
    Mell P, Grance T, (2011) The NIST definition of cloud computing, p 7Google Scholar
  24. 24.
    Tharam D, Chen W (2010) Cloud computing: issues and challenges. In: Presented at the 2010 IEEE 24th international conference on advanced information networking and applications (AINA 2010), Perth, WAGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.Innovative Technologies LaboratoryEST, Sidi Mohamed Ben Abdellah UniversityFezMorocco
  2. 2.ENSA, Sidi Mohamed Ben Abdellah UniversityFezMorocco

Personalised recommendations