TRANSFERRING STUDY PROCESS INTO VIRTUAL ENVIRONMENT: WHY IS IT NEEDED?
DOI:
https://doi.org/10.17770/sie2019vol5.3847Keywords:
e – Learning, Learning Analytics, Moodle, Virtual Learning EnvironmentAbstract
The paper presents a comparative analysis of the opinion regarding the advantages and disadvantages of virtual environment Moodle among lecturers and students in 2015 and in 2018 and correlations with increased use of Virtual Learning Environment (VLE) to improved study outcomes. The study has revealed that virtual learning environments are beneficial for the study process as they create new learning opportunities, increase access to learning material and allow time and space flexibility. The purpose of this study is to examine the use of Moodle in a college type higher education institution in 2018 and compare the results of the survey to those obtained in a similar survey in 2015 in the same college, as well as relating the change in the use of Moodle to the change in the study outcomes. The interviewees were the lecturers and students who answered questions related to the use of Moodle. The implemented learning environment includes 14 feature creation functions and 7 resources. The evaluation results indicate that Moodle is commonly used to deliver course content, develop a course plan, evaluate, create activities, and communicate with course participants. Among many functions offered by Moodle only some of them are considered to be very important and commonly used, such as tasks, reviews, tests and workshops.
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References
Bourne J., Harris D., & Mayadas F. (2005). Online Engineering Education: Learning Anywhere, Anytime. Journal of Asynchronous Learning Networks, 9:1, 5.
Caliskan, S.; & Bicen, H. (2016). Determining the perceptions of teacher candidates on the effectiveness of Moodle used in flipped education. Procedia Coputer Science, 102, 654 –658.
Campagni, R. et al. (2015). Data mining models for student careers. Expert Systems with Applications, 42(2015), 5508 – 5521.
Filippidi, A., Tselios, N., & Komis, V. (2010). Impact of Moodle Usage Practices on Students’ Performance in the Context of a Blended Learning Environment. In SALL2010: Social Applications for Life Long Learning, Patras, Nov 4-5, 2010, 2-7.
Hewson, C. (2012). Can online course based assessment methods be fair and equitable? Reltioships between students' preferences and performance within online and offline assessments. JComput Assist Learn, 28(5), 488–98.
Jo, I.-H., Kim, D., & Yoon, M. (2014). Analyzing the Log Patterns of Adult Learners in LMS Using Learning Analytics. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (LAK ’14), 183-187.
Kyburienė, L., & Juodeika, A. (2015). Mokymosi Moodle aplinkoje vertinimas: studentų požiūris. Studijos šiuolaikinėje visuomenėje, 6(1), 153-162. Retrieved from: https://www.slk.lt/sites/default/files/studijos_siuolaikineje_visuomeneje_2015.pdf
Larusson, J.A., & White, B. (2014). Learning Analytics: From Research to Practice. Springer Science + Business Media. New York.
Macfadyen, L.P., & Dawson, S. (2010). Mining LMS Data to Develop an “Early Warning System” for Educators: A Proof of Concept. Computers & Education, 54(2), 588-599.
Milevski, Z., & Zdavev, Z. (2013). Education Data Mining with Moodle 2.4. Faculty of Computer Science, Goce Delcev University – Stip. Yearbook.
Oproiu, G.C. (2015). A Study about Using E-learning Platform (Moodle) in University Teaching Process. Procedia-Social and Behavioral Sciences, 180, 426- 432.
Romero, C.; & Ventura, S. (2013). Data mining in education. Wire's Data Mining Knowledge Discovery. John Wiley and Sons, Inc..12 – 27.
Sunita, B.A. (2014). Educational Data Mining with R and Rattle. River Publishers.
Siemens, G. & Baker, R.S.J. (2012). dLearning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254.
Whitmer, J. (2012). Logging On to Improve Achievement: Evaluating the Relationship between Use of the Learning Management System, Student Characteristics, and Academic Achievement in a Hybrid Large Enrollment Undergraduate Course. Retrieved from: https://johnwhitmerdotnet.files.wordpress.com/2013/01/jwhitmer_dissertation_complete_1-21-2013.pdf
Yu, T., & Jo, I. (2014). Educational Technology Approach toward Learning Analytics: Relationship between Student Online Behavior and Learning Performance in Higher Education. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 269-270.
