DOI QR코드

DOI QR Code

Developing a National Data Metrics Framework for Learning Analytics in Korea

  • Received : 2017.04.03
  • Accepted : 2017.04.18
  • Published : 2017.04.30

Abstract

Educational applications of big data analysis have been of interest in order to improve learning effectiveness and efficiency. As a basic challenge for educational applications, the purpose of this study is to develop a comprehensive data set scheme for learning analytics in the context of digital textbook usage within the K-12 school environments of Korea. On the basis of the literature review, the Start-up Mega Planning model of needs assessment methodology was used as this study sought to come up with negotiated solutions for different stakeholders for a national level of learning metrics framework. The Ministry of Education (MOE), Seoul Metropolitan Office of Education (SMOE), and Korean Education and Research Information Service (KERIS) were involved in the discussion of the learning metrics framework scope. Finally, we suggest a proposal for the national learning metrics framework to reflect such considerations as dynamic education context and feasibility of the metrics into the K-12 Korean schools. The possibilities and limitations of the suggested framework for learning metrics are discussed and future areas of study are suggested.

Keywords

Acknowledgement

This work was supported by the Seoul Metropolitan Office of Education (SMOE) & Korea Education and Research Information Service (KERIS).

References

  1. Choi, J., Jung, S., Lee, Y., & Kim, J. (2014). The issue report about the analysis of teaching-learning model using digital textbook (KERIS Publication No. RM 2014-11). Seoul: Korean Education and Research Information Service.
  2. Ferguson, R., & Shum, S. B. (2012, April). Social learning analytics: Five approaches. Paper presented at the Second International Conference on Learning Analytics and Knowledge, Vancouver, BC.
  3. Forbes, R., Forbes, D., & Hoskins, P. (2005). Start-up mega planning: a case history. Performance Improvement Quarterly, 18(3), 100-110.
  4. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57.
  5. Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of mooc videos. Paper presented at the First ACM Conference on Learning at Scale, Atlanta, GA. Retrieved from http://dl.acm.org/citation.cfm?id=2566239.
  6. Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge, and Learning, 19, 221-240.
  7. IMS Global Learning Consortium (2013). Learning measurement for analytics whitepaper. Retrieved from http://www.imsglobal.org/IMSLearningAnalyticsWP.pdf.
  8. Jo, I. H., Kim, D., & Yoon, M. (2014, March). Analyzing the log patterns of adult learners in LMS using learning analytics. Paper presented at the Forth International Conference on Learning Analytics and Knowledge, Indianapolis, IN.
  9. Jonassen, D. H., & Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61-79.
  10. Jones, J. C. (1992). Design methods (2nd ed.). New York, NY: John Wiley & Sons, Inc.
  11. Kaufman, R. (1992). Strategic planning plus: an organizational guide. Thousand Oaks, CA: Sage Publications.
  12. Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learninginstruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757-798.
  13. Kwon, O. (2013). Data Analytics in Education: Current and Future Directions. Journal of Intelligence and Information Systems, 19(2), 87-100.
  14. Lukarov, V., Chatti, M. A., Thus, H., Kia, F. S., Muslim, A., Greven, C., & Schroeder, U. (2014). Data Models in Learning Analytics. Retrieved from http://ceur-ws.org/Vol-1227/paper22.pdf.
  15. Mager, R., & Pipe, P. (1984). Analyzing performance problems. Belmont, CA: Pitman.
  16. McKillip, J. (1987). Needs analysis: Tools for the human services and education. Newbury Park, CA: Sage Publications.
  17. Park, Y., & Jo, I. (2014). Design and application of visual dashboard baseed on learning analytics. The Journal of Educational Information and Media, 20(2), 191-216.
  18. Rha, I., Lim, C., & Cho, Y. (2014, November). Creative Use of Big Data in Education. The 1st International Forum on Big Data Analysis for Learning Improvement. Creative Use of Big Data in Education Projcet, Seoul National University, Seoul, Korea.
  19. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133-148.
  20. Yoo, K., & Yo, C. (2013). A study on the Application Method of Cadastral Information Big Data. Journal of Korean Association of Cadastre Information, 15(2), 31-51.