Skip to main content

Visual Analytics for Increasing Efficiency of Higher Education Institutions

  • Conference paper
  • First Online:
Business Information Systems Workshops (BIS 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 183))

Included in the following conference series:

Abstract

Higher education institutions have a major interest in increasing the educational quality and its effectiveness. Student retention and graduation levels constitute a particularly important quality measure of their effort. Academic Analytics is the business intelligence term used in academic settings. It especially facilitates creation of actionable intelligence to enhance learning and student success. Exploration and interactive visualization of multivariate data without significant reduction of dimensionality remains a challenge. Visual Analytics tools like Motion Charts show changes over time by presenting animations within two-dimensional space. In this paper, we present the Visual Analytics tool EDAIME intended for exploratory analysis of Academic Analytics. The tool supports various interactive data visualization methods and especially concerns with implementation of enhanced Motion Charts concept adjusted to academic settings. We utilize the capabilities of the tool in order to confirm the hypothesis concerning student retention. We also describe the design and the implementation of the interactive data visualization tool in detail.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    https://uhdspace.uhasselt.be/dspace/bitstream/1942/718/1/relational.pdf

  2. 2.

    http://is.muni.cz

  3. 3.

    http://www-958.ibm.com/software/data/cognos/manyeyes

  4. 4.

    http://developers.google.com/chart/interactive/docs/gallery/motionchart

  5. 5.

    http://www.gapminder.org

  6. 6.

    http://mbostock.github.io/protovis

  7. 7.

    http://prefuse.org

  8. 8.

    http://flare.prefuse.org

  9. 9.

    http://d3js.org

  10. 10.

    http://raphaeljs.com

  11. 11.

    http://processing.org

  12. 12.

    http://www.jmp.com

  13. 13.

    http://www.sas.com

References

  1. Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007)

    Article  Google Scholar 

  2. Delavari, N., Phon-Amnuaisuk, S., Beikzadeh, M.R.: Data mining application in higher learning institutions. Inf. Educ. 7(1), 31–54 (2008)

    Google Scholar 

  3. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)

    Google Scholar 

  4. Goldstein, P.J.: Academic analytics: the uses of management information and technology in higher education. ECAR Res. Study 8 (2005)

    Google Scholar 

  5. Oblinger, D., Campbell, J.P.: Academic Analytics. EDUCAUSE Center for Applied Research, Wahington, DC (2007)

    Google Scholar 

  6. Campbell, J.P., DeBlois, P.B., Oblinger, D.G.: Academic analytics: a new tool for a new era. EDUCAUSE Rev. 42(4), 40–57 (2007)

    Google Scholar 

  7. Battista, V., Cheng, E.: Motion charts: telling stories with statistics. In: JSM Proceedings, Statistical Computing Section, Alexandria, pp. 4473–4483 (2011)

    Google Scholar 

  8. Tversky, B., Morrison, J.B., Betrancourt, M.: Animation: can it facilitate? Int. J. Human-Comput. Stud. 57, 247–262 (2002)

    Article  Google Scholar 

  9. Bederson, B.B., Boltman, A.: Does animation help users build mental maps of spatial information? In: INFOVIS ’99: Proceedings of the 1999 IEEE Symposium on Information Visualization (1999)

    Google Scholar 

  10. Baudisch, P., Tan, D., Collomb, M., Robbins, D., Hinckley, K., Agrawala, M., Zhao, S., Ramos, G.: Phosphor: explaining transitions in the user interface using afterglow effects. In: UIST ’06: Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology (2006)

    Google Scholar 

  11. Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Trans. Vis. Comput. Graph. 14, 1325–1332 (2008)

    Article  Google Scholar 

  12. Heer, J., Robertson, G.: Animated transitions in statistical data graphics. IEEE Trans. Vis. Comput. Graph. 13, 1240–1247 (2007)

    Article  Google Scholar 

  13. Few, S.: Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Burlingame (2009)

    Google Scholar 

  14. Bayer, J., Bydžovská, H., Géryk, J., Obšívač, T., Popelínský, L.: Predicting drop-out from social behaviour of students. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 103–109 (2012)

    Google Scholar 

  15. Géryk, J.: Visual analytics by animations in higher education. In: Proceedings of the 12th European Conference on e-learning ECEL 2013, pp. 565–572 (2013)

    Google Scholar 

  16. Al-Aziz, J., Christou, N., Dinov, I.D.: SOCR motion charts: an efficient, open-source, interactive and dynamic applet for visualizing longitudinal multivariate data. J. Stat. Educ. 18(3), 1–29 (2010)

    Google Scholar 

  17. Grossenbacher, A.: The globalisation of statistical content statistical journal of the IAOS. J. Int. Assoc. Official Stat. 25, 133–144 (2008)

    Google Scholar 

  18. Vermylen, J.: Visualizing Energy Data Using Web-Based Applications. Trans. American Geophysical Union 89: Fall Meet (2008)

    Google Scholar 

  19. Olmos, M., Corrin, L.: Academic analytics in a medical curriculum: enabling educational excellence. Australas. J. Educ. Technol. 28(1), 1–15 (2012)

    Google Scholar 

  20. Few, S.: Visualizing change: an innovation in time-series analysis. In: Visual Business Intelligence Newsletter, White paper SAS (2007)

    Google Scholar 

  21. Dwyer, T.: Scalable, versatile and simple constrained graph layout. Comput. Graph. Forum 28, 991–998 (2009)

    Article  Google Scholar 

  22. de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer, New York (2000)

    Book  Google Scholar 

  23. Tikhonova, A., Ma, K.L.: A scalable parallel force-directed graph layout algorithm. In: Proceedings of the 8th Eurographics Conference on Parallel Graphics and Visualization, pp. 25–32 (2008)

    Google Scholar 

  24. Baepler, P., Murdoch, C. J.: Academic Analytics and Data Mining in Higher Education. Int. J. Sch. Teach. Learn. 4 (2010)

    Google Scholar 

Download references

Acknowledgements

We thank Michal Brandejs and Knowledge Discovery Lab for their assistance. This work has been partially supported by Faculty of Informatics, Masaryk University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Géryk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Géryk, J., Popelínský, L. (2014). Visual Analytics for Increasing Efficiency of Higher Education Institutions. In: Abramowicz, W., Kokkinaki, A. (eds) Business Information Systems Workshops. BIS 2014. Lecture Notes in Business Information Processing, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-319-11460-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11460-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11459-0

  • Online ISBN: 978-3-319-11460-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics