Skip to main content
Springer Nature Link
Log in

A Review on Visualization of Educational Data in Online Learning

  • Conference paper
  • First Online:
Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

  • 1324 Accesses

Abstract

Higher educational institutions capture huge amounts of educational data, especially in online learning. Data mining techniques have shown promises to interpret these data using different patterns . However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. Visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. This review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rabbany, R., Elatia, S., Takaffoli, M., Zaïane, O.R.: Collaborative learning of students in online discussion forums: a social network analysis perspective. In: Peña-Ayala, A. (ed.) Educational Data Mining. SCI, vol. 524, pp. 441–466. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02738-8_16

    Chapter  Google Scholar 

  2. Jordao, V.R., Gama, S., Goncalves, D.: Visualizing sequential educational datamining patterns. Int. J. Creative Interfaces Comput. Graph. 7, 1–8 (2016)

    Google Scholar 

  3. Heer, J., Bostock, M., Ogievetsky, V.: A tour through the visualization zoo: a survey of powerful visualization techniques, from the obvious to the obscure. ACM Queue 8(5), 1–22 (2010)

    Article  Google Scholar 

  4. Ware, C.: Information Visualization, Perception for Design, 2nd edn. Morgan Kaufmann, Burlington (2004)

    Google Scholar 

  5. Slater, S., Joksimovic, S., Kovanovic, V., Baker, R.S., Gasevic, D.: Tools for educational data mining: a review. J. Educ. Behav. Stat. 42(1), 1–21 (2016)

    Google Scholar 

  6. Shahiria, A.M., Husaina, W., Rashida, N.A.: A review on predicting student’s performance using data mining techniques. Proc. Comput. Sci. 72, 414–422 (2015)

    Article  Google Scholar 

  7. Dutt, A., Ismail, M.A., Herawan, T.: Asystematic review of educational data mining. IEEE Access 5, 15991–16005 (2017)

    Article  Google Scholar 

  8. Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telemat. Inform. 37, 13–49 (2019)

    Article  Google Scholar 

  9. Wu, M., Dewan, M.A.A., Lin, F., Murshed, M.: Visualization of course discussion forums: a short review from online learning perspective. In: Canadian Conference on Electrical and Computer Engineering, Edmonton, Canada (2019)

    Google Scholar 

  10. Xiaoya, G., Kan, L., Ping, L.: Visual analysis of college students’ scores in English test. In: International Conference on Computer Science and Education, Nanning, China (2009)

    Google Scholar 

  11. Trimm, D., Rheingans, P., desJardins, M.: Visualizing student histories using clustering and composition. IEEE Trans. Visual. Comput. Graph. 18(12), 2809–2818 (2012)

    Google Scholar 

  12. Xiaohuan, W., Guodong, Y., Huan, W., Wei, H.: Visual exploration for time series data using multivariate analysis method. In: International Conference on Computer Science and Education, Colombo, Sri Lanka (2013)

    Google Scholar 

  13. Dernoncourt, F., Do, C., Halawa, S.: MoocViz: a large scale, open access, collaborative, data analytics platform for MOOCs. In: NIPS Education Workshop, Nevada, USA (2013)

    Google Scholar 

  14. Paiva, R., Bittencourt, I.I., Dermeval, D.: Visualizing learning analytics and educational data mining outputs. In: International Conference on Artificial Intelligence in Education, London, UK (2018)

    Google Scholar 

  15. Cohen, W.W.: Fast effective rule induction. In: Internationation Conference on Machine Learning (1995)

    Google Scholar 

  16. Mazza, R., Dimitrova, V.: Generation of graphical representations of student tracking data in course management system. In: IEEE International Conference on Information Visualization, London, UK (2005)

    Google Scholar 

  17. OpenDX, Open Source Visualization Data explorer. https://en.wikipedia.org/wiki/IBM_OpenDX

  18. Mazza, R.: Monitoring an online course with the GISMO tool: a case study. J. Interact. Learn. Res. 18(2), 251–265 (2007)

    Google Scholar 

  19. Kuosa, K., et al.: Interactive visualization tools to improve learning and teaching in online learning environments. Int. J. Distance Educ. Technol. 14(1), 1–21 (2015)

    Article  Google Scholar 

  20. Gama, S., Goncalves, D.: Visualizing large quantities of educational datamining information. In: International Conference on Information Visualization, Paris, France (2014)

    Google Scholar 

  21. Martins, T., Goncalves, D., Gama, S.: Visualizing historical patterns in large educational datasets. Int. J. Creative Interfaces Comput. Graph. 9(1), 32–48 (2018)

    Article  Google Scholar 

  22. Goncalves, A.F.D., Maciel, A.M.A., Rodrigues, R.L.: Development of a data mining education framework for data visualization in distance learning environments. In: International Conference on Software Engineering and Knowledge Engineering (2017)

    Google Scholar 

  23. Topirceanu, A., Grosseck, G.: Decision tree learning used for the classification of student archetypes in online courses. Proc. Comput. Sci. 112, 51–61 (2017)

    Article  Google Scholar 

  24. Doko, E., Bexheti, L.A., Hamiti, M., Prevalla, B.: Sequential pattern mining model to identify the most important or difficult learning topics via mobile technologies. Int. J. Interact. Mob. Technol. 12(4), 109–122 (2018)

    Article  Google Scholar 

  25. Wortman, D., Rheingans, P.: Visualizing trends in student performance across computer science courses. In: SIGCSE Technical Symposium on Computer Science Education, Kentucky, USA (2007)

    Google Scholar 

  26. Chen, Y., Chen, Q., Zhao, M., Boyer, S., Veeramachaneni, K., Qu, H.: DropoutSeer: visualizing learning patterns in massive ppen online courses for dropout reasoning and prediction. In: IEEE Conference on Visual Analytics Science and Technology, Baltimore, USA (2016)

    Google Scholar 

Download references

Acknowledgement

This research was supported by NSERC Discovery Grant, and Academic and Professional Development Grant, Athabasca University, Canada.

Author information

Authors and Affiliations

  1. School of Computing and Information Systems, Athabasca University, Edmonton, Canada

    M. Ali Akber Dewan, Walter Moreno Pachon & Fuhua Lin

Authors
  1. M. Ali Akber Dewan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Walter Moreno Pachon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fuhua Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Ali Akber Dewan .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dewan, M.A.A., Pachon, W.M., Lin, F. (2021). A Review on Visualization of Educational Data in Online Learning. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66906-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics