Skip to main content
SpringerLink
Log in

Personalising Learning with Learning Analytics: A Review of the Literature

  • Conference paper
  • First Online:
Blended Learning. Education in a Smart Learning Environment (ICBL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12218))

Included in the following conference series:

Abstract

Advances have been made in personalised learning by the developments in learning analytics, where useful information can be extracted from educational data and analysed to devise personalised learning solutions. This paper presents a review of the literature in this area, covering a total of 144 relevant empirical articles published between 2012 and 2019 collected from Scopus. It identifies the patterns in the use of learning analytics to personalise learning in terms of the environments (what), stakeholders (who), objectives (why) and methods (how). The results show a clear growth in the number of practices, and diversity in terms of the learning contexts where learning analytics was implemented; the types of data collected; the groups of target stakeholders; the objectives of learning analytics practices; the personalised learning goals; and the analytics methods. The findings also reveal the emergence of practices related to the teacher perspective and some areas which have not been fully addressed, such as personalised intervention for future work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Patrick, S., Kennedy, K., Powell, A.: Mean What You Say: Defining and Integrating Personalized, Blended and Competency Education. International Association for K-12 Online Learning (iNACOL), Vienna (2013)

    Google Scholar 

  2. Li, K.C., Wong, B.T.-M.: How learning has been personalised: a review of literature from 2009 to 2018. In: Cheung, S.K.S., Lee, L.-K., Simonova, I., Kozel, T., Kwok, L.-F. (eds.) ICBL 2019. LNCS, vol. 11546, pp. 72–81. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21562-0_6

    Chapter  Google Scholar 

  3. Siemens, G., Gašević, D.: Special issue on learning and knowledge analytics. Educ. Technol. Soc. 15(3), 1–163 (2012)

    Google Scholar 

  4. Kravčík, M., Ullrich, C., Igel, C.: The potential of the internet of things for supporting learning and training in the digital age. In: Zlatkin-Troitschanskaia, O., et al. (eds.) Positive Learning in the Age of Information, pp. 399–412. Springer, Wiesbaden (2018). https://doi.org/10.1007/978-3-658-19567-0_24

    Chapter  Google Scholar 

  5. Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM (2012)

    Google Scholar 

  6. Godwin-Jones, R.: Scaling up and zooming in: big data and personalization in language learning. Lang. Learn. Technol. 21(1), 4–15 (2017)

    Google Scholar 

  7. Kalz, M.: Lifelong learning and its support with new technologies. In: Smelser and Baltes (eds.) International Encyclopedia of the Social and Behavioral Sciences, pp. 93–99. Oxford, Pergamon (2014)

    Google Scholar 

  8. Gabarre, S., Gabarre, C., Din, R.: Personalizing learning: a critical review of language learning with mobile phones and social networking sites. J. Adv. Res. Dyn. Control Syst. 10(2), 1782–1786 (2018)

    Google Scholar 

  9. Wong, B.T.M., Li, K.C.: A review of learning analytics intervention in higher education (2011–2018). J. Comput. Educ. 7(1), 7–28 (2020)

    Google Scholar 

  10. Santos, O.C., Kravčík, M., Boticario, J.G.: Preface to special issue on user modelling to support personalization in enhanced educational settings. Int. J. Artif. Intell. Educ. 26(3), 809–820 (2016)

    Google Scholar 

  11. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics. Int. J. Technol. Enhanced Learn. 4(5–6), 318–331 (2013)

    Google Scholar 

  12. Yau, J.Y.K., Hristova, Z.: Evaluation of an extendable context-aware “Learning Java” app with personalized user profiling. Technol. Knowl. Learn. 23, 315–330 (2018)

    Google Scholar 

  13. Mejia, C., Florian, B., Vatrapu, R., Bull, S., Gomez, S., Fabregat, R.: A novel web-based approach for visualization and inspection of reading difficulties on university students. IEEE Trans. Learn. Technol. 10(1), 53–67 (2016)

    Google Scholar 

  14. Ma, N., Xin, S., Du, J.Y.: A peer coaching-based professional development approach to improving the learning participation and learning design skills of in-service teachers. J. Educ. Technol. Soc. 21(2), 291–304 (2018)

    Google Scholar 

  15. Lan, A.S., Waters, A.E., Studer, C., Baraniuk, R.G.: Sparse factor analysis for learning and content analytics. J. Mach. Learn. Res. 15(1), 1959–2008 (2014)

    MathSciNet  MATH  Google Scholar 

  16. Xing, W., Du, D.: Dropout prediction in MOOCs: using deep learning for personalized intervention. J. Educ. Comput. Res. 57(3), 547–570 (2019)

    Google Scholar 

  17. van der Merwe, A., du Toit, T., Kruger, H.: A prescriptive specialized learning management system for academic feedback towards improved learning. J. Comput. Sci. 14(10), 1329–1340 (2018)

    Google Scholar 

  18. Shivanagowda, G.M., Goudar, R.H., Kulkarni, U.P.: CRETAL: a personalized learning environment in conventional setup. In: Proceedings of the 10th Annual ACM India Compute Conference, pp. 143–148. ACM (2017)

    Google Scholar 

  19. Berge, Z.L.: If you think socialisation in mLearning is difficult, try personalisation. Int. J. Mob. Learn. Organ. 5(3/4), 231–238 (2011)

    Google Scholar 

  20. Scott, E., Soria, A., Campo, M.: Adaptive 3D virtual learning environments – a review of the literature. IEEE Trans. Learn. Technol. 10(3), 262–276 (2017)

    Google Scholar 

  21. Li, K.C., Wong, B.T.M., Ye, C.J.: Implementing learning analytics in higher education: the case of Asia. Int. J. Serv. Stan. 12(3/4), 293–308 (2018)

    Google Scholar 

  22. Wong, B.T.M.: Learning analytics in higher education: an analysis of case studies. Asian Assoc. Open Univ. J. 12(1), 21–40 (2017)

    Google Scholar 

  23. Tabaa, Y., Medouri, A.: LASyM: a learning analytics system for MOOCs. Int. J. Adv. Comput. Sci. Appl. 4(5), 113–119 (2013)

    Google Scholar 

  24. Clow, D.: MOOCs and the funnel of participation. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 185–189. ACM (2013)

    Google Scholar 

  25. Chou, C.Y., et al.: Open student models of core competencies at the curriculum level: using learning analytics for student reflection. IEEE Trans. Emerg. Top. Comput. 5(1), 32–44 (2015)

    Google Scholar 

  26. Liu, D.Y.-T., Bartimote-Aufflick, K., Pardo, A., Bridgeman, A.J.: Data-driven personalization of student learning support in higher education. In: Peña-Ayala, A. (ed.) Learning Analytics: Fundaments, Applications, and Trends. SSDC, vol. 94, pp. 143–169. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52977-6_5

    Chapter  Google Scholar 

  27. Hellas, A., et al.: Predicting academic performance: a systematic literature review. In: Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, pp. 175–199. ACM (2018)

    Google Scholar 

  28. Papamitsiou, Z., Karapistoli, E., Economides, A.A.: Applying classification techniques on temporal trace data for shaping student behavior models. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 299–303. ACM (2016)

    Google Scholar 

  29. Callies, S., Gravel, M., Beaudry, E., Basque, J.: Logs analysis of adapted pedagogical scenarios generated by a simulation serious game architecture. Int. J. Game-Based Learn. 7(2), 1–19 (2017)

    Google Scholar 

  30. Khalil, M., Ebner, M.: Learning analytics: principles and constraints. In: Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2015, Montréal, Canada, pp. 1326–1336 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Open University of Hong Kong, Ho Man Tin, Hong Kong, China

    Kam Cheong Li & Billy Tak-Ming Wong

Authors
  1. Kam Cheong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Billy Tak-Ming Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Billy Tak-Ming Wong .

Editor information

Editors and Affiliations

  1. The Open University of Hong Kong, Hong Kong, China

    Simon K. S. Cheung

  2. University of Canterbury, Christchurch, New Zealand

    Richard Li

  3. Kasetsart University, Bangkok, Thailand

    Kongkiti Phusavat

  4. Kasetsart University, Bangkok, Thailand

    Naraphorn Paoprasert

  5. City University of Hong Kong, Kowloon, Hong Kong

    Lam‑For Kwok

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, K.C., Wong, B.TM. (2020). Personalising Learning with Learning Analytics: A Review of the Literature. In: Cheung, S., Li, R., Phusavat, K., Paoprasert, N., Kwok, L. (eds) Blended Learning. Education in a Smart Learning Environment. ICBL 2020. Lecture Notes in Computer Science(), vol 12218. Springer, Cham. https://doi.org/10.1007/978-3-030-51968-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-51968-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51967-4

  • Online ISBN: 978-3-030-51968-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation