Advertisement

Personalised English Language Education Through an E-learning Platform

  • Vladimír BradáčEmail author
  • Pavel Smolka
Conference paper
  • 211 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

The article aims at modern processes in order to get most out of student’s progress in an e-learning course. This desired progress is achieved by adopting a new methodology, which incorporated innovative features to enable the creation of a personalised study plan for students in a given course (in our case it means a course of the English language). The new features include two blocks. A block integrating a questionnaire to find out student’s sensory preferences and a block of student’s knowledge. Such blocks served as input values and information to create and verify the tested e-learning course. The suggested methodology made use of exiting capabilities of an e-learning platform Moodle, namely conditioned progress though a course. It also integrated new elements so that a new individual study plan would be created in an automated way. Such a complex system served as a testing unit to verify its functionality. We use a group of bachelor students studying at our institution.

Keywords

Intelligent tutoring systems Distance education Adaptive systems e-learning Language education 

Notes

Acknowledgements

This paper was supported by the internal grant SGS05/PRF/2019.

References

  1. 1.
    Bos, E., van de Plassche, J.: A knowledge-based, English verb-form tutor. J. Artif. Intell. Educ. 5, 107–129 (1994)Google Scholar
  2. 2.
    Rudak, L.: Susceptibility to e-teaching. In: ICT for Competitiveness, Karviná, pp. 10–16 (2012)Google Scholar
  3. 3.
    Murphy, M., McTear, M.: Learner modelling for intelligent CALL. In: Jameson, A., Paris, C., Tasso, C. (eds.) User Modeling. ICMS, vol. 383, pp. 301–312. Springer, Vienna (1997).  https://doi.org/10.1007/978-3-7091-2670-7_31CrossRefGoogle Scholar
  4. 4.
    El Hmoudová, D., Milková, E.: Computer-based testing in the field of foreign language assessment. In: Efficiency and Responsibility in Education, Czech University of Life Sciences, Prague (2012)Google Scholar
  5. 5.
    Kostolanyová, K.: Teorie adaptivního e-learningu. Ostravská univerzita, Ostrava (2012)Google Scholar
  6. 6.
    Samson, D.: An architecture for web-based e-learning promoting re-usable adaptive educational e-content. Educ. Tech. Soc. 5, 27–37 (2002)Google Scholar
  7. 7.
    Bradáč, V.: Adaptive model to support decision-making in language e-learning. In: International Conference on Education and New Learning Technologies - Proceedings, Barcelona, pp. 4036–4045 (2013)Google Scholar
  8. 8.
    Habiballa, H.: Using software package LFLC 2000. In: 2nd International Conference Aplimat 2003, Bratislava, pp. 355–358 (2003)Google Scholar
  9. 9.
    Bradáč, V.: Enhacing assessment of students’ knowledge using fuzzy logic in e-learning. In: 10th International Scientific Conference on Distance Learning in Applied Informatics, pp. 251–262. Wolters Kluwer, Štůrovo (2014)Google Scholar
  10. 10.
  11. 11.
    Leite, W.L.: Attempted validation of the scores of the VARK: learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educ. Psychol. Measur. 70(2), 323–339 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of OstravaOstravaCzech Republic

Personalised recommendations