Status of Learning Analytics in Asia: Perspectives of Higher Education Stakeholders

  • Kam Cheong LI
  • Carmen Jiawen YE
  • Billy Tak-Ming WONG
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 843)

Abstract

Despite the growing popularity of learning analytics in higher education, its development status in Asia was barely studied. This paper reports a study on the development of learning analytics in higher education in Asia. Semi-structured interviews were conducted with eight senior managers or senior academics from various tertiary institutions in Asia. The participants were asked about their institutions’ position on learning analytics, the progress in its implementation, factors leading to effective implementation, and challenges encountered, if any. The results showed that in those institutions where learning analytics has been implemented, it aimed mainly at enhancing student retention, pedagogy and student learning experience. Its effective implementation relies on support from senior management, and taking students’ views into account in decision-making. The participants’ institutions encountered difficulties due to teachers’ and students’ concerns, such as the increased workload and data privacy, as well as technical issues in data collection, processing and analysis. In short, though starting late in Asia, learning analytics has been gradually gaining attention and is being implemented. The future directions of research and practices in learning analytics are also discussed.

Keywords

Learning analytics Higher education Tertiary institutions Asia 

Notes

Acknowledgement

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/IDS16/15).

References

  1. 1.
    Siemens, G.: Learning analytics: the emergence of a discipline. Am. Behav. Sci. 57(10), 1380–1400 (2013)CrossRefGoogle Scholar
  2. 2.
    Shum, B.S.: Learning analytics. UNESCO policy brief (2012). http://iite.unesco.org/pics/publications/en/files/3214711.pdf
  3. 3.
    Siemens, G.: Learning analytics: envisioning a research discipline and a domain of practice. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada, pp. 4–8 (2012)Google Scholar
  4. 4.
    Avella, J.T., Kebritchi, M., Nunn, S. G., Kanai, T.: Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learn. 20(2) (2016). http://olj.onlinelearningconsortium.org/index.php/olj/article/view/790/201
  5. 5.
    Sclater, N., Peasgood, A., Mullan, J.: Learning analytics in higher education. A review of UK and international practice. Full report. JISC, 4(22.04) (2016)Google Scholar
  6. 6.
    Drachsler, H., Greller, W.: The pulse of learning analytics understandings and expectations from the stakeholders. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 120–129, Vancouver, Canada (2012)Google Scholar
  7. 7.
    Xiong, Q.E., Zhang, X.H.: The strategies of learning analytics in the practice of teaching innovation in higher education. Mod. Distance Educ. 5, 56–61 (2015). (in Chinese)Google Scholar
  8. 8.
    Ochoa, X., Suthers, D., Verbert, K., Duval, E.: Analysis and reflections on the third learning analytics and knowledge conference (LAK 2013). J. Learn. Anal. 1(2), 5–22 (2014)CrossRefGoogle Scholar
  9. 9.
    LACE: LACE Evidence Hub. http://evidence.laceproject.eu/. Accessed 19 Jan 2018
  10. 10.
    Arroway, P., Morgan, G., O’Keefe, M., Yanosky, R.: Learning analytics in higher education. Research report. Louisville, CO: ECAR, EDUCAUSE (2016)Google Scholar
  11. 11.
    Leitner, P., Khalil, M., Ebner, M.: Learning Analytics in Higher Education—A Literature Review. In: Peña-Ayala, A. (ed.) Learning Analytics: Fundaments, Applications, and Trends. SSDC, vol. 94, pp. 1–23. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52977-6_1CrossRefGoogle Scholar
  12. 12.
    Gao, Y., Fu, G.S.: Review of studies on learning analysis in education in China. China Med. Educ. Technol. 30(1), 23–26 (2016)MathSciNetGoogle Scholar
  13. 13.
    Jiang, Q., Zhao, W., Li, Y.F., Li, S.: Research on learning analytics dashboard based on big data. China Educ. Technol. 1, 113–119 (2017)Google Scholar
  14. 14.
    Meng, L.L., Gu, X.Q., Li, Z.: The comparison of learning analytics tools. Educ. Res. 20(4), 66–75 (2014)Google Scholar
  15. 15.
    Zhang, Y.H., Wang, F.Q., Han, Q.F., Lv, J.: Research on evaluation model for political and daily performance of students based on big data. Fujian Comput. 9, 124–127 (2015)Google Scholar
  16. 16.
    Ma, J., Han, X., Yang, J., Cheng, J.: Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: the role of the instructor. Internet High. Educ. 24, 26–34 (2014)CrossRefGoogle Scholar
  17. 17.
    Jo, I.: On the LAPA (Learning Analytics for Prediction & Action) model suggested. Future Research Seminar. Korea Society of Knowledge Management (2012)Google Scholar
  18. 18.
    Jo, I.-H., Yu, T., Lee, H., Kim, Y.: Relations between student online learning behavior and academic achievement in higher education: a learning analytics approach. In: Chen, G., Kumar, V., Kinshuk, {.}., Huang, R., Kong, S.C. (eds.) Emerging Issues in Smart Learning. LNET, pp. 275–287. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-44188-6_38CrossRefGoogle Scholar
  19. 19.
    Ogata, H., Houb, B., Li, M., Uosakic, N., Mouri, K., Liu, S.: Ubiquitous learning project using life-logging technology in Japan. Educ. Technol. Soc. 27(2), 85–100 (2014)Google Scholar
  20. 20.
    Sorour, S.E., Goda, K., Mine, T.: Correlation of topic model and student grades using comment data mining. In: Proceeding of the 46th ACM Technical Symposium on Computer Science Education, Kansas City, USA, pp. 441–446 (2015)Google Scholar
  21. 21.
    Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, A., Kojima, K., Yamada, M.: E-book based learning analytics in university education. In: Proceeding of the 23rd International Conference on Computers in Education, Asia Pacific Society for Computers in Education, China, pp. 401–406 (2015)Google Scholar
  22. 22.
    Pratheesh, N., Devi, T.: Necessity of learning analytics in software engineering education. J Eng. Sci. Technol. 10(3), 269–281 (2015)Google Scholar
  23. 23.
    Boulanger, D., Seanosky, J., Kumar, V., Kinshuk, Panneerselvam, K., Somasundaram, T.S.: Smart learning analytics. In: Chen, G., Kumar, V., Kinshuk, Huang, R., Kong, S.C. (eds.) Emerging issues in Smart Learning. Lecture Notes in Educational Technology, pp. 289–296. Springer, Berlin (2015).  https://doi.org/10.1007/978-3-662-44188-6_39CrossRefGoogle Scholar
  24. 24.
    Howell, J.A., Roberts, L.D., Seaman, K., Gibson, D.C.: Are we on our way to becoming a “Helicopter University”? Academics’ views on learning analytics. Technol. Knowl. Learn. 23, 1–20 (2017). https://link.springer.com/article/10.1007/s10758-017-9329-9CrossRefGoogle Scholar
  25. 25.
    Slade, S., Prinsloo, P.: Student perspectives on the use of their data: Between intrusion, surveillance and care. European J. Open Distance E-learn. 18(1) (2015). http://www.eurodl.org/index.php?p=special&sp=articles&inum=6&abstract=672&article=679
  26. 26.
    Roberts, L.D., Howell, J.A., Seaman, K., Gibson, D.C.: Student attitudes toward learning analytics in higher education: “the fitbit version of the learning world”. Frontiers in Psychology 7 (2016). https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01959/full

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Kam Cheong LI
    • 1
  • Carmen Jiawen YE
    • 2
  • Billy Tak-Ming WONG
    • 1
  1. 1.The Open University of Hong KongHo Man TinHong Kong
  2. 2.Caritas Institute of Higher EducationTseung Kwan OHong Kong

Personalised recommendations