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Exploring Data Visualization as an Emerging Analytic Technique

, University of Texas at Austin, United States ; , , , , Univ. of Texas at Austin, United States

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Vancouver, British Columbia, Canada ISBN 978-1-939797-31-5 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA


Visual analytics have emerged as a way to allow researchers to understand big data in diverse learning contexts We are interested in using visualization techniques to examine learners’ behavior patterns in an adaptive learning environment and explore the relationship between performance and behavior patterns Participants were first-year students entering into a pharmacy professional degree program As part of a large research effort, in this study we focused on high and low performing students The findings showed the visualizations confirmed some findings of the statisitical analyses and at the same time revealed the nuanced interesting findings that can be missed otherwise Combining with traditional statistical analyses with visualization techniques has provided a more detailed picture of learners’ behaviors in an adaptive learning envuronment Such research should provide useful insights about using analytics to understand how learners use an adaptive learning system


Liu, M., Kang, J., Zilong, P., Zou, W. & Lee, H. (2017). Exploring Data Visualization as an Emerging Analytic Technique. In J. Dron & S. Mishra (Eds.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1681-1690). Vancouver, British Columbia, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved December 9, 2018 from .

View References & Citations Map


  1. Dernoncourt, F., Do, C., Halawa, S., O’Reilly, U.-M., Taylor, C., Veeramachaneni, K., & Wu, S. (2013, December). MoocViz: A Large Scale, Open Access, Collaborative, Data Analytics Platform for MOOCs. Paper presented at the NIPS Workshop on Data-Driven Education, Lake Tahoe, Nevada, USA.
  2. Graf, S., & Liu, T.C. (2010). Analysis of learners' navigational behavior and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131. Doi:10.1111/J.1365-2729.2009.00336.x
  3. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC Horizon Report: 2016 Higher Education Edition. Austin, TX: The New Media Consortium.
  4. Koedinger, K., Cunningham, K., Skogsholm, A., & Leber, B. (2008). An open repository and analysis tools for finegrained, longitudinal learner data. In International conference on Educational Data Mining (pp. 157–166).
  5. Liu, M., Lee, J., Kang, J & Liu, S. (2016). What we can learn from the data: A multiple-case study examining behavior patterns by students with different characteristics in using a serious game. The Technology, Knowledge and Learning Journal, 21(1), 33-57. Doi:10.1007/s10758-015-9263-7
  6. Melero, J., Hernández‐Leo, D., Sun, J., Santos, P., & Blat, J. (2015). How was the activity? A visualization support for a case of location-based learning design. British Journal of Educational Technology, 46(2), 317-329.
  7. Premlatha, K.R., Dharani, B., & Geetha, T.V. (2014). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments, 24(6), 1-22.
  8. Qu, H., & Chen, Q. (2015). Visual analytics for MOOC data. IEEE Computer Graphics& Applications, 35(6), 69– 75.
  9. Shi, C., Fu, S., Chen, Q., & Qu, H. (2015, April). VisMOOC: Visualizing video clickstream data from massive open online courses. In 2015 IEEE Pacific Visualization Symposium (PacificVis) (pp. 159-166). IEEE.
  10. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
  11. Siemens, G., & Baker, R.S.J.D. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
  12. Snow, E.L., Allen, L.K., Jacovina, M.E., & McNamara, D.S. (2015). Does agency matter? Exploring the impact of controlled behaviors within a game-based environment. Computers& Education, 82, 378-392.

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