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Adapting Treemaps to Student Academic Performance Visualization

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 943))

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Abstract

Treemap visualization method is applied to student academic performance via an empirical study. This approach is developed to facilitate educational decision-making. The case study provides analyzing and classifying of hierarchical academic data and allows decision-makers to modify features of the visual platform dynamically.

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References

  1. Raji, M., Duggan, J., DeCotes, B., Huang, J., Zanden, B.V.: Visual progression analysis of student records data. arXiv preprint arXiv:1710.06811 (2017)

  2. Bueckle, M.G.N.S.A., Börner, K.: Empowering instructors in learning management systems: interactive heat map analytics dashboard (2017). Accessed 2 Nov 2017

    Google Scholar 

  3. Emmons, S.R., Light, R.P., Börner, K.: MOOC visual analytics: Empowering students, teachers, researchers, and platform developers of massively open online courses. J. Assoc. Inf. Sci. Technol. 68(10), 2350–2363 (2017)

    Article  Google Scholar 

  4. Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S.: Web usage mining for predicting final marks of students that use Moodle courses. Comput. Appl. Eng. Educ. 21(1), 135–146 (2013)

    Article  Google Scholar 

  5. Mazza, R., Milani, C.: GISMO: a graphical interactive student monitoring tool for course management systems. In: International Conference on Technology Enhanced Learning, Milan, pp. 1–8, November 2004

    Google Scholar 

  6. Mazza, R., Dimitrova, V.: Visualising student tracking data to support instructors in web-based distance education. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 154–161. ACM, May 2004

    Google Scholar 

  7. Lakshmi, T.M., Martin, A., Venkatesan, V.P.: An analysis of students performance using genetic algorithm. J. Comput. Sci. Appl. 1(4), 75–79 (2013)

    Google Scholar 

  8. Doctor, F., Iqbal, R.: An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarisation. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE, June 2012

    Google Scholar 

  9. Ogor, E.N.: Student academic performance monitoring and evaluation using data mining techniques. In: Electronics, Robotics and Automotive Mechanics Conference, 2007. CERMA 2007, pp. 354–359. IEEE, September 2007

    Google Scholar 

  10. Donnellan, D., Pahl, C.: Data mining technology for the evaluation of web-based teaching and learning systems. In: E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 747–752. Association for the Advancement of Computing in Education (AACE) (2002)

    Google Scholar 

  11. El-Halees, A.: Mining Students Data to Analyze e-Learning Behavior: A Case Study (2009)

    Google Scholar 

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

    Article  Google Scholar 

  13. Vliegen, R., Van Wijk, J.J., van der Linden, E.J.: Visualizing business data with generalized treemaps. IEEE Trans. Visual Comput. Graphics 12(5), 789–796 (2006)

    Article  Google Scholar 

  14. Jungmeister, W.A., Turo, D.: Adapting Treemaps to Stock Portfolio Visualization (1992)

    Google Scholar 

  15. Keivanpour, S., Ait Kadi, D.: Strategic eco-design map of the complex products: toward visualization of the design for environment. Int. J. Prod. Res. 56(24), 1–17 (2017)

    Google Scholar 

  16. Wattenberg, M.: Visualizing the stock market. In CHI 1999 Extended Abstracts on Human Factors in Computing Systems, pp. 188–189. ACM, May 1999

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management, Information and Supply Chain, Thompson Rivers University, Kamloops, Canada

    Samira Keivanpour

Authors
  1. Samira Keivanpour
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Corresponding author

Correspondence to Samira Keivanpour .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Keivanpour, S. (2020). Adapting Treemaps to Student Academic Performance Visualization. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_54

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