Open Access Subscription Access
Analysis of Factors Influencing LMS Extracted Data using Learning Analysis on the Total Score of Learners
The development of information and communication technology has led to change in today's modern society with various names, such as knowledge, information society, internationalization society, and especially, the development of distance education without time and space restrictions. With the development of distance education, problems of dropouts and incompleteness of learners who are constantly in trouble are emerging as and they must be solved. Therefore, in this study, log data (regularity of learning start interval, total number of learnings and total learning time) and personal background data (courses experience, education experience, gender and age) accumulated in the web through learning management system (LMS) in a distant education environment. The purpose of this study is to analyze the effect of age and educational experience on the total scores that determine completion (more than 60 points) and incompleteness (less than 60 points). The study was carried out with data from 1,130 learners of distance learning centers, which were conducted for a total of 16 weeks. Data was extracted and analyzed based on learning analysis, and the results were as follows: First, among the log data, it was found that the total learning time and number of learning had significant effects on the total score. As the number of access to the LMS increased, the learning time and total score increased. Second, among personal background data, age was found to have a significant effect on the total score. It was concluded that the probability of completing the study, i.e, the probability of completing the study, increases as age increases, so the purpose of learning becomes more apparent. The data used in this study was used when a learner started signing up for LMS for learning and collected the consent in advance with the consent of the personal information agreement.
Learning Analytics, Lifelong Distance Education, LMS (Learning Management System), Log data, Personal Background data.
- J. H. Kim, “Impact of Learner's Time Management Strategieson Achievement: A Learning Analytics Approach”, The Graduate School of Ewha Womans University, Seoul Korea, 2013.
- Y. M. Kim, “Impact of Regularity of Learning Interval, Total Learning Hours and Number of Access to E-learning in a Corporate E-learning Environment on Academic Achievement”, The Graduate School of Ewha Womans University, Seoul Korea, 2011.
- S. Y. Kim, “An Analysis of College Student Dropouts' Mobility Paths and Structure”, The Journal of Educational Studies, Vol. 43, No. 3, pp. 131-163, 2012.
- K. G. Kim, “Learner Activity Modeling Based on Teaching and Learning Activities Data”, Software and Data Eng, Vol. 5, No. 9, pp. 411 418, 2016.
- S. T. Kim, “A correlation between Learning Behavior and Achievement Level of Learners in e-Learning,” Korea University of Technology and Education, Chungcheongnam-do, Korea, 2003.
- E. H. Kim, M. K. Park, Coreference resolution of Korean anaphoric zero objects: Towards a supervised machine learning approach. International Journal of Computer Science and Information Technology for Education. Vol. 1. No. 1. Dec. 2016. GV Press. pp:1-6.
- M. S. Kang, J. I. Kim, and I. W. Park, “The Examination of the Variables related to the Students' e-learning Participation that Have an Effect on Learning Achievement in e-learning Environment of Cyber University”, Korean Society For Internet Information, Vol. 10, No. 5, pp. 135-143, 2009.
- S. Y. Kwon, “The Analysis of differences of learners' participation, procrastination, learning time and achievement by adult learners' adherence of learning time schedule in e-Learning environments”, Journal of Learner- Centered Curriculum and Instruction, Vol. 9, No. 3, pp. 1-86, 2009.
- Y. R. Kim, “Investigation of the relationship between university students characteristics and dropout factors”, Graduate School of Education, Kyungpook National University, Kyungpook Korea, 2012.
- J. Y. Shin, O. R Jeong and D. S. Cho, “The Analysis of individual Learning Status on Webbased Instruction”, The Korean Association of Computer Education, Vol. 6, No.3, pp. 107-120, 2003.
- J. H. Shin, J. W. Choi and W. Koh, “A study on the Use of Learning Analytics in Higher Education: Focusing on the perspective of professors”, Journal of Educational Technology, Vol. 31, No. 2, pp. 223-252, 2015.
- J. W. You, “Dropout Prediction Modeling and Investigating the Feasibility of Early Detection in e-Learning Courses”, The Korean Association of Computer Education, Vol. 17, No.1, pp. 1-12, 2014.
- J. Lee, “LCMS(Learning Content Management System) based e-Learning Development and Application” , Korean Association for
- Educational Information and Media, Vol. 8, No. 2, pp. 93-113, 2002.
- H. Y. Lee, “Development of prediction models based on the clustered online learners' behavioral patterns in university e-Learning environment”, Graduate School of Ewha Womans University, Seoul Korea, 2016.
- Y. W. Lim, “A substantial study on the Relationship between students' variables and dropout in Cyber university”, Journal & Artical management System, Vol. 11, No. 2, pp. 205-219, 2007.
- M. L. Ahn, Y. Y. Choi, Y. H. Bae, Y. M. Ko, and M. H. Kim, “A Literature Review on Learning Analytics: Exploratory study of empirical
- researches utilizing log data in Korea”, Journal of Educational Technology. Vol. 32, No. 2, pp. 253-291, 2016.
- J. Y. Chung, M. S. Sun, and M. J. Jeong, “An Analysis of Institutional Factors Affecting on College Dropout Rates” Education Research Institut, Vol. 16, No. 4, pp. 57-76, 2015.
- Y. S. Cho, and R. J. Abel, “Prospects for the Application of Learning Analytics” RM 2013-15, KERIS.
- I. H. Cho, “Learning analysis and learning design, development of the convergence horizon”, Journal of Educational Technology, Vol. 2015, No. 2, pp. 422-434, 2015.
- B. M. Chang. Cross-Cultural Distance Learning Project for Promoting English Language Proficiency and Cultural Awareness of University Students in Korea. International Journal of Computer Science and Information Technology for Education. Vol. 1. No. 1. Dec
- I. H .Cho, Y. J. Park, and J. H. Kim, Understanding Learning Analytics, Seoul : Young sa Park, 2019, pp. 76-92.
- Y. J. Joo, W. J. Shim, and S. M. Kim, “A Study on the Factors Affecting the Drop-out in Corporate Cyber Learning”, The Journal of Educational Information and Media, Vol. 14, No. 4, pp. 5-25, 2008.
- Y. H. Jeong, “Education Analytics”, MEDIA & EDUCATION, Vol. 5, No. 1, pp. 44-49, 2015.
- B. Y. Jeong, “Analyses of Learning Achievement and Satisfaction on Demographic Characteristics in Cyber Universities : A Case Study”, The Journal of Educational Information and Media, Vol. 10, No. 3, pp. 127-150, 2004.
- D. H. Han, and B. J. Jeon, “Analysis of Learning Type Factors that Affect e-learning Performance : Centering on the Comparison Analysis of Whole Learners Log and Excellent Learners”, Journal of the Korean Data Analysis Society, Vol. 17, No. 2, pp. 897-912, 2015.
- J. S. Han, Y. M Kim and M. K. Kang, "Suggestions to Support 'Flipped Learning' on Stage Based on the Survey of Primary and Secondary Teachers' Recognition.Asia-Pacific Journal of Educational Management Research. Vol. 1. No. 1. Dec. 2016. GVPress. pp:145-154.
- S. R. Hwang, “Impact of Learner's Learning Behavior on Achievement: The Moderating Effect of Learning Motivation”, The Graduate School of Education Ewha Womans University, Seoul Korea, 2016.
- Dietz-Uhler, B., & Hurn, J. E. Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, Vol. 12, No. 1, 17 26. 2013.
- Dyckhoff, A. L., Zielke, D., Bultmann, M., Chatti, M. A., & Schroeder, U, “Design and implementation of a learning analytics toolkit for teachers”, Educational Technology & Society, Vol. 15, No.3, pp.58-76, 2012.
- Elias, T, Learning Analytics: Definitions, Processes, and Potential. Creative Commons, 2011.
- Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. The 2011 horizon report. Austin, Texas: The New Media Consortium, 2011.
- Hall, R, “Schedules of Practical Work For the Analysis of Case Studies of Learning and Development”, The Journal of the learning science, Vol.10, No.1-2, pp.203-222, 2001.
- Liao, S. H., Chu, P. H., & Hsiao, P. Y, “Data mining techniques and applications–A decade review from 2000 to 2011”, Expert Systems with Applications, Vol. 39, No. 12, pp.11303-11311, 2012.
- Long, P, & Siemens, G, Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, Vol. 46, No.5, pp.30-32,
- Macfadyen, L. P., & Dawson, S, “Numbers are not enough. why e-learning analytics failed to inform an institutional strategic plan”, Educational Technology & Society, Vol. 15, No. 3, pp. 149-163, 2012.
- Rau, W., & Durand, A, “The Academic Ethic and College Grades: Does Hard Work Help Students to "Make the Grade"?”, Sociology of Education, Vol. 73, No.1, pp.19-38, 2000.
- Shum, S, B, LEARNING ANALYTICS, UNESCO Institute for Information Technologies in Education, Policy Brief. November 2012.
- Tinto, V, Leaving college: rethinking the causes and cures of student ttrition. Chicago: University of Chicago Press, 1987.
- Wagner, E., & Ice, P, “Data changes everything: Delivering on the promise of learning analytics in higher education”, EDUCAUSE Review, Vol. 47, No. 4, pp. 32-36. 2012.
- Zorrilla, M. E., Menasalvas, E., Marin, D., Mora, E., & Segovia, J, Web usage mining project for improving web-based learning sites. In Web Mining Workshop Cataluna(pp.1-22), 2015.
- Seo, J.T., Kim, Y.G. & Ju, R. (2020). The Effect of LMS Data on Total Score of Learner's in Lifelong Distance Education Center: A Learning Analytical Approach. Journal of Education and Social Science (JESS), HolyKnight, vol. 1, 15 22. doi: 10.46410/jess.2020.1.1.03.
Abstract Views: 3
PDF Views: 0