Advertisement

Technological Support of Teaching in the Area of Creating a Personalized E-course of Informatics

  • Milan Turčáni
  • Zoltán BaloghEmail author
Conference paper
  • 16 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1135)

Abstract

The paper deals with the issue of finding and assessing the effectiveness of the use of information technologies for the optimal setting of teaching procedures. One possible solution is to personalize the teaching of computer science subjects in terms of accepting constructivist approaches. An important part of this form of teaching is the involvement of procedures and activities known from eLearning support of teaching, which depend not only on the way of providing study materials but also on activities related to the verification of students’ knowledge. An important component of providing knowledge, with a view to developing learners’ cognitive and intellectual abilities, is the adaptive approach of the education system to learners. The paper is devoted to the design of a methodology for creating a personalized e-course that meets the higher attributes and requirements for personalization of teaching with the possibility of adapting to the learner in a given environment. The paper also includes a survey of the current state of perception of university students towards the use of mobile technologies in the area of learning.

Keywords

Information technologies Teaching personalization E-course Adaptive learning Mobile learning Petri nets 

Notes

Acknowledgements

This paper was created with the financial support of the projects: 1. The project KEGA 036UKF-4/2019, Adaptation of the learning process using sensor networks and the Internet of Things; 2. Research and Innovation for the project Fake news on the Internet - identification, content analysis, emotions (code: NFP313010T527).

References

  1. 1.
    Kostolanyova, K., Sarmanova, J., Takacs, O.: Classification of learning styles for adaptive education. New Educ. Rev. 23(1), 199–212 (2011)Google Scholar
  2. 2.
    Drlik, M., Skalka, J.: Virtual faculty development using top-down implementation strategy and adapted EES model. In: Yalin, H.I., Adiloglu, F., Boz, H., Karatas, S., Ozdamli, F. (eds) World Conference on Educational Technology Researches-2011, Procedia Social Behavioral Science. Elsevier Science Bv, Amsterdam, vol. 28 (2011).  https://doi.org/10.1016/j.sbspro.2011.11.117.CrossRefGoogle Scholar
  3. 3.
    Molnar, G., Szuts, Z.: Advanced mobile communication and media devices and applications in the base of higher education. In: Proceedings of the 2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY), pp. 169–174 (2014)Google Scholar
  4. 4.
    Petrova, K., Li, C.: Focus and setting in mobile learning research: a review of the literature. Innovation and knowledge management in twin track economies: challenges & solutions. In: International Business Information Management Association-IBIMA, vol. 1–3, Norristown (2009)Google Scholar
  5. 5.
    Moore, J.L., Dickson-Deane, C., Galyen, K.: e-Learning, online learning, and distance learning environments: are they the same? Internet High Educ. 14(2), 129–135 (2011).  https://doi.org/10.1016/j.iheduc.2010.10.001CrossRefGoogle Scholar
  6. 6.
    Liu, Y., Li, H.X., Carlsson, C.: Factors driving the adoption of m-learning: an empirical study. Comput. Educ. 55(3), 1211–1219 (2010).  https://doi.org/10.1016/j.compedu.2010.05.018CrossRefGoogle Scholar
  7. 7.
    Lowenthal, J.N.: Using mobile learning: determinates impacting behavioral intention. Am. J. Distance Educ. 24(4), 195–206 (2010).  https://doi.org/10.1080/08923647.2010.519947CrossRefGoogle Scholar
  8. 8.
    Hubalovsky, S., Hubalovska, M., Musilek, M.: Assessment of the influence of adaptive e-learning on learning effectiveness of primary school pupils. Comput. Hum. Behav. 92, 691–705 (2019).  https://doi.org/10.1016/j.chb.2018.05.033CrossRefGoogle Scholar
  9. 9.
    Kostolanyova, K., Sarmanova, J., Czeczotkova, B.: Analysis of teaching styles of teachers in the contex of e-learning. Information and Communication Technology in Education, Univ Ostrava, Ostrava 1 (2010)Google Scholar
  10. 10.
    Preidys, S., Sakalauskas, L.: Analysis of students’ study activities in virtual learning environments using data mining methods. Technol. Econ. Dev. Econ. 16(1), 94–108 (2010).  https://doi.org/10.3846/tede.2010.06CrossRefGoogle Scholar
  11. 11.
    Pruett, L.: 4 Steps Towards a More Personal Classroom (2018). https://www.teachthought.com/pedagogy/getting-started-personalized-learning/
  12. 12.
    Balogh, Z., Turčáni, M., Magdin, M.: Design and creation of a universal model of educational process with the support of Petri nets. Lecture Notes in Electrical Engineering, vol. 269 (2014).  https://doi.org/10.1007/978-94-007-7618-0_103Google Scholar
  13. 13.
    Balogh, Z, Turcani, M.: Possibilities of modelling web-based education using IF-THEN rules and fuzzy petri nets in LMS. In: AbdManaf, A., Zeki, A., Zamani, M., Chuprat, S., ElQawasmeh, E. (eds.) Informatics Engineering and Information Science, Communications in Computer and Information Science, Pt I, vol 251, pp. 93–106 (2011)CrossRefGoogle Scholar
  14. 14.
    Balogh, Z., Turcani, M., Magdin, M., Burianova, M.: Creating model educational processes using Petri nets implemented in the LMS. In: Kvasnicka, R. (ed.) Efficiency and Responsibility in Education 2012, pp. 7–16 (2012)Google Scholar
  15. 15.
    Khan, B.H., Ally, M. International Handbook of E-Learning Volume 1: Theoretical Perspectives and Research (2015).  https://doi.org/10.4324/9781315760933CrossRefGoogle Scholar
  16. 16.
    El Fazazi, H., Qbadou, M., Salhi, I., Mansouri, K.: Personalized recommender system for e-Learning environment based on student’s preferences. Int. J. Comput. Sci. Netw. Secur. 18(10), 173–178 (2018)Google Scholar
  17. 17.
    Zounek, J., Juhaňák, L., Staudková, H., Poláček, J.: E-learning. Učení (se) s digitálními technologiemi [E-learning. Learning with digital technologies] (2016)Google Scholar
  18. 18.
    Nakić, J., Graf, S., Granić, A.: Exploring the adaptation to learning styles: the case of AdaptiveLesson module for moodle. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 7946 (2013).  https://doi.org/10.1007/978-3-642-39062-3_33Google Scholar
  19. 19.
    Houska, M., Berankova, M.H.: Pedagogical efficiency of multimedia lectures on mathematical methods in economics. In: Proceedings of the 7th International Conference Efficiency and Responsibility in Education 2010, Czech University Life Sciences Prague, Prague 6 (2010)Google Scholar
  20. 20.
    Melia, J.M.J., Gonzalez-Such, J., Garcia-Bellido, M.R.: Evaluative research and information and communication technology (ICT). Rev. Esp. Pedag. 70(251), 93–110 (2012)Google Scholar
  21. 21.
    Balogh, Z., Turcani, M., Magdin, M.: The possibilities of using Petri Nets for realization of a universal model of educational process. In: Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration (IRI 2013), pp 162–169. IEEE (2013)Google Scholar
  22. 22.
    Ismail, I., Bokhare, SF., Azizan, S.N, Azman, N.: Teaching via mobile phone: a case study on Malaysian teachers’ technology acceptance and readiness. J. Educ. Online 10(1) (2013).  https://doi.org/10.9743/jeo.2013.1.3
  23. 23.
    Burgerova, J., Cimermanova, I.: Creating a sense of presence in online learning environment. In: Proceedings of the 10th International Scientific Conference on Distance Learning in Applied Informatics (DiVAI 2014). Wolters Kluwer Cr a S, Praha 3 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Informatics, Faculty of Natural SciencesConstantine the Philosopher University in NitraNitraSlovakia

Personalised recommendations