Advertisement

Prognostication of Student’s Performance: An Hierarchical Clustering Strategy for Educational Dataset

  • Parag Bhalchandra
  • Aniket Muley
  • Mahesh Joshi
  • Santosh Khamitkar
  • Nitin Darkunde
  • Sakharam Lokhande
  • Pawan Wasnik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)

Abstract

The emerging field of educational data mining gives us better perspectives for insights in educational data. This is done by extracting hidden patterns in educational databases. In these lines, the objective of this research work is to introduce hierarchical clustering models for student’s collected data. The ultimate goal is to find attributes in terms of set of clusters which severely affect the student’s performance. Here clustering is intentionally used as the most common causes affecting performance within the database which cannot be seen normally. The results enable us to use discovered characteristic or patterns in palpating student’s learning outcomes. These patterns can be useful for teachers to identify effective prognostication strategies for students.

Keywords

Educational data mining Clustering Performance analysis Pattern discovery 

References

  1. 1.
    Linoff, G., Michael J, et al.: Data Mining Techniques, 3rd edn. Wiley Publications (2011)Google Scholar
  2. 2.
    Dunham, M.: In: Dunham, M.H. (ed.) Data Mining: Introductory and Advanced Topics. Pearson publications (2002)Google Scholar
  3. 3.
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. The Morgan Kaufmann Series in Data Management Systems, Jim Gray (2006)Google Scholar
  5. 5.
    Behrouz, et al.: Predicting Student Performance: An Application of Data Mining Methods with the Educational Web-Based System Lon-CAPA. IEEE, Boulder (2003) Google Scholar
  6. 6.
    IBM SPSS Statistics 22 Documentation on Internet. www.ibm.com/support/docview.wss?uid=swg27038407
  7. 7.
    Cortez, P., Silva, A.: Using Data Mining To Predict Secondary School Student Performance. http://www.researchgate.net/publication/Using_data_mining_to_predict_secondary_school_student_performance
  8. 8.
    Pritchard, M.E., Wilson, G.S.: Using emotional and social factors to predict student success. J. Coll. Student Dev. 44(1), 18–28 (2003)CrossRefGoogle Scholar
  9. 9.
    Ali, S., et al.: Factors contributing to the students academic performance: a case study of Islamia University Sub-Campus. Am. J. Educ. Res. 1(8), 283–289 (2013)CrossRefGoogle Scholar
  10. 10.
    Graetz, B.: Socio-economic status in education research and policy in John Ainley et al., Socio-economic Status and School Education DEET/ACER Canberra. J. Pediatr. Psychol. 20(2), 205–216 (1995)CrossRefGoogle Scholar
  11. 11.
    Considine, G., Zappala, G.: Influence of social and economic disadvantage in the academic performance of school students in Australia. J. Sociol. 38, 129–148 (2002)CrossRefGoogle Scholar
  12. 12.
    Bratti, M., Staffolani, S.: Student Time Allocation and Educational Production Functions. University of Ancona Department of Economics Working Paper No. 170 (2002)Google Scholar
  13. 13.
    Ma, Y., Liu, B., Wong, C.K., Yu, P.S., Lee, S.M.: Targeting the right Students using data mining. In: Sixth ACM SIGKDD International Conference, Boston, MA (Conference Proceedings) pp. 457–464 (2000)Google Scholar
  14. 14.
    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 system LON-CAPA. In: Proceedings of ASEE/IEEE Frontiers in Education Conference. IEEE, Boulder, CO (2003)Google Scholar
  15. 15.
    Kotsiantis, S.: Educational data mining: a case study for predicting dropout—prone students. Int. J. Knowl. Eng. Soft Data Paradigms 1(2), 101–111 (2009)CrossRefGoogle Scholar
  16. 16.
    Berkhin, P.: Survey of Clustering Data Mining Techniques, Accrue Software. www.cc.gatech.edu/~isbell/reading/papers/berkhin02survey.pdf
  17. 17.
    Sasirekha, K., Baby, P.: Agglomerative hierarchical clustering algorithm—a review. Int. J. Sci. Res. Publ. 3(3) (2013). ISSN 2250-3153Google Scholar
  18. 18.
    Murugesan, K., Zhang, J.: Hybrid hierarchical clustering: an experimental analysis. Technical Report: CMIDA-hipsccs #001-11. www.cs.uky.edu/~jzhang/pub/techrep.html

Copyright information

© Springer India 2016

Authors and Affiliations

  • Parag Bhalchandra
    • 1
  • Aniket Muley
    • 2
  • Mahesh Joshi
    • 3
  • Santosh Khamitkar
    • 1
  • Nitin Darkunde
    • 2
  • Sakharam Lokhande
    • 1
  • Pawan Wasnik
    • 1
  1. 1.School of Computational SciencesS.R.T.M. UniversityNandedIndia
  2. 2.School of Mathematical SciencesS.R.T.M. UniversityNandedIndia
  3. 3.School of Educational SciencesS.R.T.M. UniversityNandedIndia

Personalised recommendations