Jelena Mamčenko, Inga Piščikienė, Brigita Šustickienė, Irma Šileikienė


The paper presents comprehensive study of applicability of Moodle virtual learning environment to the development of practical skills. The quantitative research involved faculty students and  lecturers of Vilnius College of Technologies and Design. Data analysis indicated that currently the most beneficial method to develop practical skills is blended method. This result, however, turned out to be more positive for students than for lecturers, the latter being less willing to employ technology alongside traditional classroom. The article also briefly describes data mining technologies that are often employed to plan study process, as well as depicts data flows and action sequence of the future survey. The study also puts forward recommendations for further research.


Blended Learning; Educational Data Mining; Practical skills; Virtual Learning Environment; Web Mining

Full Text:



Ayesha, A., Mustafa, T., Khan, M. I. (2010). Data Mining Model for Higher Education System, European Journal of Scientific Research, vol.43, no.1, 24-29.

Amandu, G.M., Muliira, J.K., Fronda, D.C. (2013). Using Moodle E-learning Platform to Foster Student Self-directed Learning: Experiences with Utilization of the Software in Undergraduate Nursing Courses in a Middle Eastern University, Procedia - Social and Behavioral Sciences 93, 677 – 683.

Barge, P., Londhe, B.R. (2014). From Teaching, Learning to Assessment: Moodle Experience at B’School in India. Procedia Economics and Finance (pp.857 – 865).

Buldu, A., Üçgün, K. (2010). Data Mining Application on Students’ Data. Procedia Social and Behavioral Sciences 2, 5251–5259.

Er, E., Özden, M., & Arifoglu, A. (2009). A Blended E-learning Environment: A Model Proposition for Integration of Asynchronous and Synchronous E-learning, International Journal of Learning, 16(2), 449-460.

Escobar-Rodriguez, T., Monge-Lozano, P. (2012). The Acceptance of Moodle Technology by Business Administration Students. Computers & Education, 58, 1085–1093.

Halees, A. (2012). Mining Students Data to Analyze e-Learning Behavior: A Case Study.

Harris, J., Mishra, P., & Koehler, M. (2009). Teachers’ Technological Pedagogical Content Knowledge and Learning Activity Types: Curriculum-based Technology Integration Reframed, Journal of Research on Technology in Education, 41(4), 393-416.

Kulvietiene, R., Sileikiene, I. (2006). The Blended Learning Design and Delivery Method, WSEAS Transactions on Information Science and Applications, 12(3), 2360-2366.

Martin-Blas, T., Serrano-Fernandez, A. (2009). The role of New Technologies in the Learning Process: Moodle as a Teaching Tool in Physics, Computers & Education, 52 (pp.35–44).

Merceron, A., Yacef, K. (2005). Educational Data Mining: a Case Study, Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED, IOS Press (pp. 20-26).

Nate, S., Zwilling, M. (2014). Student data mining solution–knowledge management system related to higher education institutions. An International Journal, 2013 Elsevier Ltd. Expert Systems with Applications 41, 6400–6407.

Papamitsiou, Z., Economides, A. (2004). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17 (4), 49–64.

Reis, L.O., Ikari, O., Taha-Neto, K.A., Gugliotta, A., Denardi, F. (2015). Delivery of a Urology Online Course Using Moodle Versus Didactic Lectures Methods, International journal of medical informatics, 84, 149–154.

Simovic, V., Kozina, G., Zupan Milkovic, Z. (2014). E-learning Systems – Support to Quality Teaching, Proceedings of the 5th International Conference on Education and Educational Technologies, Malasia.

Tuijnman, A., Boström, A. K. (2002). Changing Notions of Lifelong Education and Lifelong Learning, International Review of Education, 48(1/2), 93–110.

Volungevičiene, A., Teresevičiene, M. (2008). Quality Assessment Dimensions of Distance Teaching/Learning Curriculum Designing, The quality of Higher education, 32-53.

Zimmermann, J., Brodersen, K. H., Pellet, J. P., August, E., Buhmann, J. M. (2011). Predicting Graduate-Level Performance from Undergraduate Achievements,. Proceedings of the 4th international conference on educational data mining, 357–358.



  • There are currently no refbacks.