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Deriving Association between Learning Behavior and Programming Skills

  • S. Charles
  • L. Arockiam
  • V. Arul Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

Data mining is the process of discovering patterns from large amount of data stored in student database. It predicts the facts from the dataset and interpreted as useful information to the society. The learning behavior of the student and programming skill are dissimilar in a classroom and programming lab. Data mining techniques are used to find association between the Learning behavior and programming skills using students’ dataset. Clustering is used to determine the similarity in the students’ dataset based on the nature of the learning behavior and programming skills. Each cluster reveals the identity based on its learning behavior of the student. Likewise the programming skill is categorized based on descriptive modeling. Multilayer Perceptron (MLP) technique classifies the learning behavior of students and their programming skills based on learning by example. The task is to determine the association between learning behavior and programming skills through descriptive and predictive modeling using mapping or function. It reveals that there is a positive correlation between student learning behavior and programming skills. This analysis could help the staff members to provide right training to the students for their improvement of programming skill.

Keywords

Multilayer Perceptron K-Means Clustering Criterion Reference Model Learning Behavior 

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References

  1. 1.
    Khalilian, M., Boroujeni, F.Z., Mustapha, N., Sulaiman, M.N.: K-Means Divide and Conquer Clustering. In: International Conference on Computer and Automation Engineering, pp. 306–309. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  2. 2.
    Kolb, D.A.: Experimental learning, Experience as the source of the learning and development. Prentice Hall Inc., Englewood Cliffs (1984)Google Scholar
  3. 3.
    Choi, S.C., Rodin, E.Y.: Statistical Methods of Discrimination and Classification. In: Advances in Theory and applications (1986)Google Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John. Wiley & sons Inc., Chichester (2000)zbMATHGoogle Scholar
  5. 5.
    Khamis, I., Idris, S.: Issues and Solutions in Assessing Object-oriented programming Skills in the Core Education of Computer Science and Information Technology. In: 12th WSEAS International Conference on Computers, Heraklion, Greece, July 23-25 (2008)Google Scholar
  6. 6.
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2008)CrossRefGoogle Scholar
  7. 7.
    Ayers, E., Nugent, R., Dean, N.: Skill Set Profile Clustering Based on Student Capability Vectors Computed from Online Tutoring Data. In: Baker, R.S.J.d., Barnes, T., Beck, J.E. (eds.) Proceedings of 1st International Conference on Educational Data Mining 2008, Montreal, Quebec, Canada, June 20-21, pp. 210–217.Google Scholar
  8. 8.
    Pavlik Jr., P.I., Cen, H., Wu, L., Koedinger, K.R.: Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. In: 1st Proceedings of International Conference on Educational Data Mining, Canada, June 20-21, pp. 77–86 (2008)Google Scholar
  9. 9.
    Green, T.M., Jeong, D.M., Fisher, B.: Using Personality Factors to Predict Interface Learning Performance. In: 43rd Hawaii International Conference on System Sciences, Honolulu, HI, January 5-8, pp. 1–10. IEEE Computer Society, Los Alamitos (2010)Google Scholar
  10. 10.
    Marshall, L., Austin, M.: The relationship between software skills and subject specific knowledge, theory and practice. Learning and Teaching Projects (2002/2003)Google Scholar
  11. 11.
    McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagen, D., Kollokant, Y., Lazer, C., Thomas, L.A., Utting, I.: A Luti national Study of Assessment of Programming Skills of First Year CS students. SIGC, SE Bulletin 33, 125–140 (2001)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Chiu, C.: Cluster Analysis for Cognitive Diagnosis:Theory and Applications. Ph.D.Dissertation, Educational Psychology, University of Illinois at Urbana Champaign (2008)Google Scholar
  14. 14.
    Ayers, E., Nugent, R., Dean, N.: A Comparison of Student Skill Knowledge Estimates. In: Proceedings of 2nd International Conference on Educational Data Mining 2009, Cordoba, Spain, July 1-3, pp. 1–10 (2009)Google Scholar
  15. 15.
    Nghe, N.T., Janecek, P., Haddawy, P.: A Comparative Analysis of Techniques for Predicting Academic Performance. Paper presented at 37th ASEE/IEEE Frontiers in Education Conference, Milwaukee, WI, October 10-13 (2007)Google Scholar
  16. 16.
    Ramaswami, M., Bhaskaran, R.: A Study on Feature Selection Techniques in Educational Data Mining. Journal of Computing 1(1) (December 2009)Google Scholar
  17. 17.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2/e. Morgan Kaufmann Publishers, San Francisco (2006)zbMATHGoogle Scholar
  18. 18.
    Arockiam, L., Charles, S., Arul Kumar, V.: Deriving Association between Personality Traits and Programming Skills. In: International Conferences on Advances in Communication, Network and Computing (October 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • S. Charles
    • 1
  • L. Arockiam
    • 1
  • V. Arul Kumar
    • 1
  1. 1.Department of Computer ScienceSt Joseph’s CollegeTiruchirappalliIndia

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