Applying Data Mining Methods to Generate Formative Feedback in Team Projects

  • Rimmal Nadeem
  • X. Rosalind Wang
  • Caslon ChuaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)


It is a challenging task to evaluate the performance of individual team members of a project, more so when there is unequal contributions by the team members. The project supervisor often provide formative feedback through assessment of weekly work logs against the agreed project plan. Automating this evaluation mechanism has the obvious advantage of providing timely feedback in an efficient manner otherwise not possible due to the workload on supervisors especially in the academic settings. We explore the design space of an automated formative assessment solution based on text data mining. We evaluate the performance of various machine learning techniques (both classification and regression versions), by tweaking their control parameters, based on the achieved accuracy of prediction. We showed that the KNN regression models, despite their simplicity, produce the most accurate result across different similarity methods. We verify these research findings by employing various data visualisation techniques such as learning curves, residual plots and goodness of fit.


Educational data mining Text mining Visualisation 


  1. 1.
    Almasoud, A.M., Al-Khalifa, H.S., Al-Salman, A.: Recent developments in data mining applications and techniques. In: 2015 Tenth International Conference on Digital Information Management (ICDIM), pp. 36–42, October 2015Google Scholar
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  3. 3.
    Deo, S.: R tutorial: residual analysis for regression, March 2016Google Scholar
  4. 4.
    Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)Google Scholar
  5. 5.
    Huebner, R.A.: A survey of educational data-mining research. Res. High. Educ. J. 19, 1–13 (2013)Google Scholar
  6. 6.
    Jin, H., Liu, H.: Research on visualization techniques in data mining. In: 2009 International Conference on Computational Intelligence and Software Engineering, pp. 1–3, December 2009Google Scholar
  7. 7.
    Le, K., Chua, C., Wang, R.: Mining software engineering team project work logs to generate formative assessment. In: International Workshop on Software Driven Big Data Analytics (SoftBDA 2017) (2017)Google Scholar
  8. 8.
    Munzner, T.: Visualization Analysis and Design. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  9. 9.
    Murray, S.: Interactive Data Visualization for the Web. O’Reilly Media, Inc., Sebastopol (2013)Google Scholar
  10. 10.
    Nguyen, T., Chua, C.: Predictive tool for software team performance. In: 2016 23rd Asia-Pacific Software Engineering Conference, pp. 373–376, December 2016Google Scholar
  11. 11.
    Obie, H.O.: Data—driven visualisations that make sense. In: 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 313–314, October 2017Google Scholar
  12. 12.
    Perlich, C.: Learning curves in machine learning, January 2011Google Scholar
  13. 13.
    Shahiri, A.M., Husain, W., Rashid, N.A.: A review on predicting student’s performance using data mining techniques. Procedia Comput. Sci. 72, 414–422 (2015)CrossRefGoogle Scholar
  14. 14.
    Thai-Nghe, N., Horvth, T., Schmidt-Thieme, L.: Personalized forecasting student performance. In: 2011 IEEE 11th International Conference on Advanced Learning Technologies, pp. 412–414, July 2011Google Scholar

Copyright information

© Crown 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUNSWSydneyAustralia
  2. 2.Data61, CSIROSydneyAustralia
  3. 3.School of Software and Electrical EngineeringSwinburne University of TechnologyMelbourneAustralia

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