An Interactive Rule Based Approach to Generate Strength Assessment Report: Graduate Student Perspective

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Data and Information or Knowledge has a significant role on human activities. Data mining means extracting or discovering knowledge from large volume of data. Now-a-days, data mining process is applying in educational field also; it is called as educational data mining. Educational Data Mining used many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others. By using these techniques, the discovered knowledge can be used for prediction and analysis purposes of student patterns. Existing techniques like tree classification and some clustering techniques are suffering with decision-making problems. To solve this problem, in this paper, an interactive approach is used to prune and filter discovered rules. In proposed system, an integrate user knowledge is used in the post processing task. A set of rules or measures are given as input to proposed system in order to evaluate the student performance. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. By applying proposed approach to discover the likelihood of student’s deviations / requiring special attention is organized and efficient providing more insight by using Strength Assessment Report. After analyzing the student performance, strength assessment reports are generated which lists the career skills or competencies that are strong/good in.


Educational Mining Association Rules Post Processing Strength Assessment Report(SAR) Competency Skills Student Performance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Heikki, M.: Data mining: machine learning, statistics, and databases. IEEE (1996)Google Scholar
  2. 2.
    Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach. Springer, New York (2007)Google Scholar
  3. 3.
    Kovačić, Z.: Early Prediction of Student Success: Mining Students Enrolment Data. In: Proceedings of Informing Science & IT Education Conference (InSITE 2010), pp. 647–665 (2010)Google Scholar
  4. 4.
    Vandamme, J., Meskens, N.: Predicting Academic Performance by Data Mining Methods. Education Economics 15(4), 405–419 (2007)CrossRefGoogle Scholar
  5. 5.
    Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Prediction of Student’s Performance in Distance Learning Using Machine Learning Techniques. Applied Artificial Intelligence 18(5), 411–426 (2004)CrossRefGoogle Scholar
  6. 6.
    Yu, C., DiGangi, S., Jannasch-Pennell, A., Kaprolet, C.: A Data Mining Approach for Identifying Predictors of Student Retention from Sophomore to Junior Year. Journal of Data Science 8, 307–325 (2010)Google Scholar
  7. 7.
    Cortez, P., Silva, A.: Using Data Mining to Predict Secondary School Student Performance. In: Brito, A., Teixeira, J. (eds.) EUROSIS, pp. 5–12 (2008)Google Scholar
  8. 8.
    Ramaswami, M., Bhaskaran, R.: A CHAID Based Performance Prediction Model in Educational Data Mining. IJCSI International Journal of Computer Science Issues 7(1(1)) (January 2010)Google Scholar
  9. 9.
    Al-Radaideh, Q.A., Al-Shawakfa, E.M., Al-Najjar, M.I.: Mining student data using decision trees. In: The Proceedings of the 2006 International Arab Conference on Information Technology (2006)Google Scholar
  10. 10.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)Google Scholar
  11. 11.
    Galit, A., et al.: Examining online learning processes based on log files analysis: a case study. Research, Reflection and Innovations in Integrating ICT in Education (2007)Google Scholar
  12. 12.
    Ayesha, S., Mustafa, T., Sattar, A.R., Inayat Khan, M.: Data mining model for higher education system. Europen Journal of Scientific Research 43(1), 24–29 (2010)Google Scholar
  13. 13.
    Romero, C., Ventura, S., Espejo, P.G., Hervás, C.: Data mining algorithms to classify students. In: International Conference on Educational Data Mining (EDM), Montreal, pp. 8–17Google Scholar
  14. 14.
    Romero, C., Ventura, S.: Educational Data Mining: a Survey from 1995 to 2005. In: Expert Systems with Applications, pp. 135–146. ElsevierGoogle Scholar
  15. 15.
    Minaei-Bidgoli, B., Kashy, D.A., Kortemeyer, G., Punch, W.F.: Predicting student performance: an application of data mining methods with the educational web-based systems. In: LONCAPA, 33rd ASEE/IEEE Frontiers in Education Conference, Boulder, pp. 13–18Google Scholar
  16. 16.
    Zekić-Sušac, M., Frajman-Jakšić, A., Drvenkar, N.: Neuron Networks and Trees of Decision-making for Prediction of Eficiency in Studies. Ekonomski Vjesnik (2), 314–327Google Scholar
  17. 17.
    Kumar, S.A., Vijayalakshmi, M.N.: Efficiency of Decision Trees in Predicting Student’s Academic Performance. In: First International Conference on Computer Science, Engineering and Applications, CS and IT 2002, Dubai, pp. 335–343 (2002)Google Scholar
  18. 18.
    Sun, H.: Research on Student Learning Result System based on Data Mining. IJCSNS International Journal of Computer Science and Network Security 10(4) (April 2010)Google Scholar
  19. 19.
    Khan, Z.N.: Scholastic achievement of higher secondary students in science stream. Journal of Social Sciences 1(2), 84–87 (2005)CrossRefGoogle Scholar
  20. 20.
    Siraj, F., Abdoulha, M.A.: Uncovering hidden Information within University’s Student Enrollment Data using Data Mining. In: Third Asia International Conference on Modeling and Simulation (2009)Google Scholar
  21. 21.
    Klosgen, W., Zytkow, J.: Handbook of data mining and knowledge discovery. Oxford University Press, New YorkGoogle Scholar
  22. 22.
    Wu, X., Kumar, V.: The Top Ten Algorithms in Data Mining. Chapman and Hall, Boca RatonGoogle Scholar
  23. 23.
    Fayadd, U., Piatesky, Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AAAI Press / The MIT Press, Massachusetts Institute Of Technology (1996) ISBN –262 56097–6Google Scholar
  24. 24.
    Hijazi, S.T., Naqvi, R.S.M.M.: Factors affecting student’s performance: A Case of Private Colleges. Bangladesh e-Journal of Sociology 3(1) (2006)Google Scholar
  25. 25.
    Bean, J.P., Metzner, B.S.: A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research (1985)Google Scholar
  26. 26.
    Murtaugh, P., Burns, L., Schuster, J.: Predicting the retention of university students. Research in Higher Education 40(3), 355–337Google Scholar
  27. 27.
    Rokach, L., Maimon, O.: Data mining with decision trees – Theory and applications. World Scientific Publishing, New JerseyGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.KL UniversityVaddeswaramIndia
  2. 2.KKR & KSR Institute of Technology and SciencesKoukondaIndia

Personalised recommendations