HolyKnight
Article Page
 
JESS

Journal of Education and Social Science

ISSN:

Social science in education and curricular development receive wide attention by many researchers and educational institutions. Each social science has its own knowledge, information, skills, perspectives and methods of investigation. Selecting, organizing and presenting social science in textbooks is a major challenge for curriculum developers, for example. The objective of this journal is to bring together research contributions on the guidelines, design, specification, and implementation of rules, architectures, protocol for current and future technological innovation for education and social science.

Topics:

  • Adult and continuing education
  • Contrast education
  • Early childhood education
  • Education policy and implementation
  • Educational Science
  • Educational Communication and Terminology
  • Education measurement and evaluation
  • Information and Communication Technology on Education
  • Language education
  • Language and creative writing
  • Inclusive Education
  • ICT in Education
  • Economics of Education
  • The role psychology in education
  • Education Leadership and Management
  • Sociology of Education
  • Pedagogy
  • Guidelines
     
    The Effect of LMS Data on Total Score of Learner’s in Lifelong Distance Education Center: A Learning Analytical Approach
    Volume 1, No. 1, June 2020 | 8 pages| http//dx.doi.org/10.46410/jess.2020.1.1.03

    Authors

    Jong-Teak Seo, Kookmin University, Korea
    Young-gi Kim, Kyunghee Cyber University, Korea
    Ra-chel Ju, Hanyang University, Korea


    Abstract

    The purpose of this study is to analyze the effects of gender, age and educational experience on total scores for determining completion (over 60) and under commission (under 60). For this study, I collected log data (such as regularity of learning start interval, total number of learning and total learning time) and personal background data (number of courses of experience). The data were collected from 1,130 learners for 15 weeks and analyzed based on their learning analysis. The results were as follows: First, among the log data, the total learning time and number of learning had significant effects on the total score. The more the number of accesses to the LMS, the longer the learning time and the higher the total score. Second, among the personal background data, age had a significant effect on the total score. As they get older, they have a clearer sense of purpose for learning, which means that they are more likely to complete. The data used in this study was used when the learner started signing up to LMS for learning and collected the consent in advance from the personal information agreement.


    Keywords

    Learning Analytics, Lifelong Distance Education, LMS (Learning Management System), Log data, Personal background data.


    References

    [1] H. Kim, “Impact of Learner’s Time Management Strategieson Achievement: A Learning Analytics Approach”, The Graduate School of Ewha Womans University, Seoul Korea, 2013.

    [2] 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.

    [3] 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).

    [4] G. Kim, “Learner Activity Modeling Based on Teaching and Learning Activities Data”, Software and Data Eng, Vol. 5, No. 9, pp. 411-418, (2016).

    [5] 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.

    [6] 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).

    [7] 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).

    [8] R. Kim, “Investigation of the relationship between university students characteristics and dropout factors”, Graduate School of Education, Kyungpook National University, Kyungpook Korea, 2012.

    [9] 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).

    [10] 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.

    [11] 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).

    [12] 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).

    [13] 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).

    [14] H. Cho, Y. J. Park, and J. H. Kim, Understanding Learning Analytics, Seoul : Young sa Park, 2019.

    [15] 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).

    [16] H. Jeong, “Education Analytics”, MEDIA & EDUCATION, Vol. 5, No. 1, pp. 44-49, (2015).

    [17] 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).

    [18] H. Han, and B. J. Chun, “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).

    [19] 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.

    [20] 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).

    [21] Elias, T, Learning Analytics: Definitions, Processes, and Potential. Creative Commons, 2011.

    [22] 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).

    [23] Long, P, & Siemens, G, Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, Vol. 46, No.5, pp.30-32, (2011).

    [24] 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).

    [25] 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).

    [26] Shum, S, B, LEARNING ANALYTICS, UNESCO Institute for Information Technologies in Education, Policy Brief. November 2012.

    [27] Tinto, V, Leaving college: rethinking the causes and cures of student ttrition. Chicago: University of Chicago Press, 1987.

    [28] 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).

    [29] 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).


    Citations

    APA:
    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.

    MLA:
    Seo, Jong-Teak, et al “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, HolyKnight, vol. 1, 2020, pp 15-22. JESS. https://holyknight.co.uk/journals/jess/vol1-3/

    IEEE:
    [1] Seo, J.T., Kim, Y.G. & Ju, R. “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, pp. 15-22. June 2020.