A Smart Mobile Application for Learning English Verbs in Mauritian Primary Schools

  • Bhoovandev Fakooa
  • Mehnaz Bibi Diouman Banon
  • Baby Gobin-RahimbuxEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)


Learning English verbs in primary schools in Mauritius may sometimes be difficult for students as English is not their mother tongue. Integrating information technology to traditional way of teaching can help to improve the teaching process. In this paper, an AI-enabled mobile application which has been developed to help the learning Mauritian students is discussed. The application provides for personalised learning and an artificial neural network analyses the learning pattern of the students to readapt the type of learning material proposed to them. To bridge the gap due to language issues, the interaction between the students and application is in Creole. The student can say the verbs in Creole and then get the translated version in the various tenses. Instructions on how to use the application is also given in Creole. Quizzes are also given to help the learning process.


Mobile learning Artificial neural network Interactive systems 



The dataset was taken with permission of the participants. All the authors are responsible for any kind of issues in future.


  1. 1.
  2. 2.
    Graf, S.: Identifying learning styles in learning management systems by using indications from students’ behavior. In: 8th IEEE International Conference on Advanced Learning Technologies, pp. 482–486. IEEE Press, Spain (2008)Google Scholar
  3. 3.
    Brusilovskt, P.: Methods and techniques of adaptive hypermedia. In: User Modeling and User-Adapted Interaction (1996)Google Scholar
  4. 4.
    Mehdipour, Y., Zerehkafi, H.: Mobile learning for education: benefits and challenges. Int. J. Comput. Eng. Res. (2013)Google Scholar
  5. 5.
    Saccol, A.Z., Reinhard, N., Schlemmer, E., Barbosa, J.L.V.: M-Learning (mobile learning) in practice: a training experience with IT professionals. J. Inf. Syst. Technol. Manag. (2010)Google Scholar
  6. 6.
  7. 7.
    Bernard, J., Chang, T., Popescu, E., Graf, S.: Learning style identifier: improving the precision of learning style identification through computational intelligence algorithms. Expert Syst. Appl. 75, 94–108 (2017)CrossRefGoogle Scholar
  8. 8.
    Mota, J.: Using learning styles and neural networks as an approach to eLearning content and layout adaptation. In: Doctoral Symposium on Informatics Engineering (2008)Google Scholar
  9. 9.
    Kolekar, S., Bormane, D.S., Sanjeevi, S.G.: Learning style recognition using artificial neural network for adaptive user interface in e-learning. In: Computational Intelligence and Computing Research (ICCIC), pp. 1–5. IEEE Press, (2010)Google Scholar
  10. 10.
    Gruber, R.T.: Toward principles for the design of ontologies used for knowledge sharing. Hum. Comput. Stud. 43(5–6), 907–928 (2005)Google Scholar
  11. 11.
    Guarino, N.: Formal Ontology and Information Systems. National Research Council (2017)Google Scholar
  12. 12.
    Khozooyi, N., Seyedi, N., Malekhoseini, R.: Ontology-based e-learning. Int. J. Comput. Sci. Inf. Technol. Secur. (2012)Google Scholar
  13. 13.
    Web Ontology Language (OWL):
  14. 14.
    Handley, Z.: Is text-to-speech synthesis ready for use in computer-assisted language learning? Speech Commun. 51(10), 906–919 (2009)CrossRefGoogle Scholar
  15. 15.
    Microsoft Azure Cloud Computing Platform & Services:
  16. 16.
    Porting of Jena to Android:

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bhoovandev Fakooa
    • 1
  • Mehnaz Bibi Diouman Banon
    • 1
  • Baby Gobin-Rahimbux
    • 1
    Email author
  1. 1.University of MauritiusReduitMauritius

Personalised recommendations