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.
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References
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)
Ferguson, R., et al.: Research evidence on the use of learning analytics - implications for education policy (2016)
Bichsel, J.: Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations. EDUCAUSE Center for Applied Research (2012)
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)
Adejo, O., Connolly, T.: Learning analytics in higher education development: a roadmap. J. Educ. Pract. 8, 156–163 (2017)
Larusson, J.A., White, B.: Learning Analytics. From Research to Practice. Springer, New York (2014)
Lang, C., Siemens, G., Wise, A., Gašević, D.: The handbook of learning analytics. Soc. Learn. Analyt. Res. (2017)
Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D.: Handbook of Educational Data Mining. Chapman & Hall/CRC (2010)
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)
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)
Mlynarska, E., Greene, D., Cunningham, P.: Time Series Clustering of Moodle Activity Data. AICS 2016 (2016)
Greller, W., Drachsler, H.: Translating learning into numbers: a generic framework for learning analytics. Educ. Technol. Soc. 15, 42–57 (2012)
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)
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)
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)
Munk, M., Benko, L.: Using entropy in web usage data preprocessing. Entropy 20, 67 (2018)
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)
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)
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.
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Drlík, M. (2020). Understanding of Time-Based Trends in Virtual Learning Environment Stakeholders’ Behaviour. In: Auer, M., Tsiatsos, T. (eds) The Challenges of the Digital Transformation in Education. ICL 2018. Advances in Intelligent Systems and Computing, vol 916. Springer, Cham. https://doi.org/10.1007/978-3-030-11932-4_31
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DOI: https://doi.org/10.1007/978-3-030-11932-4_31
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