Using Association Rule Mining to Find the Effect of Course Selection on Academic Performance in Computer Science I

  • Lebogang Mashiloane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)

Abstract

It is important for first year students in higher educational institutions to get the best advice and information with regards to course selection and registration. During registration students select the courses and number of courses they would like to enroll into. The decisions made during registration are done with the assistance of academics and course coordinators. This study focuses on the first year Computer Science students and their overall academic performance in first year. Computer Science I has Mathematics as a compulsory co-requisite, therefore after selecting Computer Science I, the students have to enroll into Mathematics and then select two additional courses. Can data mining techniques assist in identifying the additional courses that will yield towards the best academic performance? Using a modified version of the CRISP-DM methodology this work applies an Association Rule Mining algorithm to first year Computer Science data from 2006 to 2012. The Apriori algorithm from the WEKA toolkit was used. This algorithm was used to select the best course combinations with Computer Science I and Mathematics I. The results showed a good relationship between Computer Science I and Biology on its own, Biology with Chemistry and Psychology with Economics. Most of the rules that were produced had good accuracy results as well. These results are consistent in related literature with areas such as Bio-informatics combining Biology and Computer Science.

Keywords

Educational Data Mining Association Rule Mining Apriori algorithm 

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References

  1. 1.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering 32(1), 71–82 (2006)Google Scholar
  2. 2.
  3. 3.
    Shearer, C.: The crisp-dm model: The new blueprint for data mining. Journal of Data Warehousing 5(4), 13–22 (2000)Google Scholar
  4. 4.
    Sunita, B., Lobo, L.: Article: Best combination of machine learning algorithms for course recommendation system in e-learning. International Journal of Computer Applications 41(6), 1–10 (2012); Published by Foundation of Computer Science, New York, USACrossRefGoogle Scholar
  5. 5.
    Vannozzi, G., Della Croce, U., Starita, A., Benvenuti, F., Cappozzo, A.: Journal of neuroengineering and rehabilitation. Journal of Neuroengineering and Rehabilitation 1, 7 (2004)CrossRefGoogle Scholar
  6. 6.
    Vialardi, C., Bravo, J., Shafti, L., Ortigosa, A.: Recommendation in higher education using data mining techniques. International Working Group on Educational Data Mining (2009)Google Scholar
  7. 7.
    Zailani, A., Tutut, H., Noraziah, A., Mustafa, M.D.: Mining significant association rules from educational data using critical relative support approach. Procedia - Social and Behavioral Sciences 28, 97 (2011), http://www.sciencedirect.com/science/article/pii/S1877042811024591; World Conference on Educational Technology Researches - 2011
  8. 8.
    Penn-Edwards, S.: They do better than us: A case study of course combinations and their impact on English assessment results. Educating: Weaving Research into Practice (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lebogang Mashiloane
    • 1
  1. 1.School of Computer ScienceUniversity of the WitwatersrandJohannesburgSouth Africa

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