Advertisement

Understanding of Time-Based Trends in Virtual Learning Environment Stakeholders’ Behaviour

  • Martin DrlíkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 916)

Abstract

The analysis of data collected from the interaction of users in the virtual learning environment attracts much attention today as a promising approach for advancing the current understanding of the learning content development, learning process in general as well as VLE stakeholders’ behaviour. The learning analytics research has not frequently focused on analysing of time-based trends in VLE stakeholders’ behaviour or their preferences in the same VLE over different years of deployment, as well as on analysing of temporal trends in the selection of different activity types over a typical period. Therefore, the paper deals with several methods, which can be used for analysing VLE stakeholders’ behaviour over several academic years. The paper introduces a case study, which shows that several analytical and data mining methods can give useful insight into the changing behaviour of the stakeholders of the VLE over a longer period. Finally, the paper summarises the obtained results and discusses possible implications and limitations of the applied approach from different perspectives in the context of the management of the virtual learning environment, VLE stakeholders and educational content improvement at the institutional level.

Keywords

Learning analytics Log analysis Virtual learning environments User behaviour Time-based trends analysis 

Notes

Acknowledgement

This work was supported by the Cultural and Educational Grant Agency of the Ministry of Education of the Slovak Republic under the contract KEGA-029UKF-4/2018 and by the project “IT Academy – Education for 21st Century” under the contract ITMS 312011F057.

References

  1. 1.
    Sclater, N.: Learning analytics. The current state of play in the UK higher and further education. JISC. Effective Learning Analytics. Using data and analytics to support students. Effective Learning Analytics. JISC (2014)Google Scholar
  2. 2.
    Ferguson, R., et al.: Research evidence on the use of learning analytics - implications for education policy (2016)Google Scholar
  3. 3.
    Bichsel, J.: Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations. EDUCAUSE Center for Applied Research (2012)Google Scholar
  4. 4.
    Siemens, G., Dawson, S., Lynch, G.: Improving the Quality and Productivity of the Higher Education Sector. Office of Learning and Teaching, Australian Government, Canberra, Australia (2014)Google Scholar
  5. 5.
    Adejo, O., Connolly, T.: Learning analytics in higher education development: a roadmap. J. Educ. Pract. 8, 156–163 (2017)Google Scholar
  6. 6.
    Larusson, J.A., White, B.: Learning Analytics. From Research to Practice. Springer, New York (2014)Google Scholar
  7. 7.
    Lang, C., Siemens, G., Wise, A., Gašević, D.: The handbook of learning analytics. Soc. Learn. Analyt. Res. (2017)Google Scholar
  8. 8.
    Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D.: Handbook of Educational Data Mining. Chapman & Hall/CRC (2010)Google Scholar
  9. 9.
    Navarro, A.M., Moreno-Ger, P.: Comparison of clustering algorithms for learning analytics with educational datasets. Int. J. Interact. Multimed. Artif. Intell. 5, 1–8 (2018)Google Scholar
  10. 10.
    Preidys, S., Sakalauskas, L.: Analysis of students’ study activities in virtual learning environments using data mining methods. Ukio Technologinis ir Ekonominis Vystymas 16, 94–108 (2010)Google Scholar
  11. 11.
    Mlynarska, E., Greene, D., Cunningham, P.: Time Series Clustering of Moodle Activity Data. AICS 2016 (2016)Google Scholar
  12. 12.
    Greller, W., Drachsler, H.: Translating learning into numbers: a generic framework for learning analytics. Educ. Technol. Soc. 15, 42–57 (2012)Google Scholar
  13. 13.
    Skalka, J., Drlík, M., Švec, P.: Knowledge discovery from university information systems for purposes of quality assurance implementation. In: 2013 IEEE Global Engineering Education Conference (EDUCON), pp. 591–596 (2013)Google Scholar
  14. 14.
    Munk, M., Drlík, M., Benko, L., Reichel, J.: Quantitative and qualitative evaluation of sequence patterns found by application of different educational data preprocessing techniques. IEEE Access 5, 8989–9004 (2017)CrossRefGoogle Scholar
  15. 15.
    Munk, M., Kapusta, J., Švec, P.: Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor. Procedia Comput. Sci. 1, 2273–2280 (2010)CrossRefGoogle Scholar
  16. 16.
    Munk, M., Benko, L.: Using entropy in web usage data preprocessing. Entropy 20, 67 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Munk, M., Drlík, M.: Chapter 10 - methodology of predictive modelling of students’ behaviour in virtual learning environment A2 - Caballé, Santi. In: Clarisó, R. (ed.) Formative Assessment, Learning Data Analytics and Gamification, pp. 187–216. Academic Press, Boston (2016)CrossRefGoogle Scholar
  18. 18.
    Sheard, J.: Basics of statistical analysis of interactions data from web-based learning environments. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (eds.) Handbook of Educational Data Mining. CRC Press, A Chapman & Hall Book (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Constantine the Philosopher University in NitraNitraSlovakia

Personalised recommendations