Application of Decision Tree for Predictive Analysis of Student’s Self Satisfaction with Multivariate Parameters

  • Aniket MuleyEmail author
  • Parag Bhalchandra
  • Govind Kulkarni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


Decision trees are better known for automatic feature selection in predictive analysis. There is no need of variable transformation while processing data in decision tree as such trees work on selection of important variables as parent nodes and they act as split node. An attempt is made herein to enumerate decision tree over customized educational dataset. The work is based on the hypothesis that distinctiveness, routine and sensitivity related variables of students have close association together with self satisfaction and motivation. This paper demonstrates better feature selection using tree analytics. Experimental analytics were carried out with R software package.


Educational analytics Decision trees Classification Multivariate analysis 


  1. 1.
    Ahuja, R., Jha, A., Maurya, R., Srivastava, R.: Analysis of educational data mining. In: Yadav, N., Yadav, A., Bansal, J.C., Deep, K., Kim, J.H. (eds.) Harmony Search and Nature Inspired Optimization Algorithms. AISC, vol. 741, pp. 897–907. Springer, Singapore (2019). Scholar
  2. 2.
    Ali, S., Haider, Z., Munir, F., Khan, H., Ahmed, A.: 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
  3. 3.
    Angiani, G., Ferrari, A., Fornacciari, P., Mordonini, M., Tomaiuolo, M.: Real marks analysis for predicting students’ performance. In: Di Mascio, T., et al. (eds.) MIS4TEL 2018. AISC, vol. 804, pp. 37–44. Springer, Cham (2019). Scholar
  4. 4.
    Bratti, M., Staffolani, S.: Student time allocation and educational production functions. Ann. Econ. Stat. 1, 103–40 (2013)CrossRefGoogle Scholar
  5. 5.
    Considine, G., Zappalà, G.: The influence of social and economic disadvantage in the academic performance of school students in Australia. J. Sociol. 38(2), 129–48 (2002)CrossRefGoogle Scholar
  6. 6.
    Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Pearson Education India (2002)Google Scholar
  7. 7.
    Field, A.: Discovering Statistics Using IBM SPSS Statistics. Sage (2013)Google Scholar
  8. 8.
    Graetz, B.: Socio-economic status in education research and policy in John A., et al. Socio-economic status and School Education DEET/ACER Canberra. J. Pediatr. Psychol. 20(2), 205–16 (1995)Google Scholar
  9. 9.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  10. 10.
    Hussain, S., Atallah, R., Kamsin, A., Hazarika, J.: Classification, clustering and association rule mining in educational datasets using data mining tools: a case study. In: Silhavy, R. (ed.) CSOC2018 2018. AISC, vol. 765, pp. 196–211. Springer, Cham (2019). Scholar
  11. 11.
    Kamal, P., Ahuja, S.: Academic performance prediction using data mining techniques: identification of influential factors effecting the academic performance in undergrad professional course. In: Yadav, N., Yadav, A., Bansal, J.C., Deep, K., Kim, J.H. (eds.) Harmony Search and Nature Inspired Optimization Algorithms. AISC, vol. 741, pp. 835–843. Springer, Singapore (2019). Scholar
  12. 12.
    Online resources information.
  13. 13.
    Pritchard, M.E., Wilson, G.S.: Using emotional and social factors to predict student success. J. Coll. Stud. Dev. 44(1), 18–28 (2003)CrossRefGoogle Scholar
  14. 14.
    R software information.
  15. 15.
    Rawat, K.S., Malhan, I.V.: A hybrid classification method based on machine learning classifiers to predict performance in educational data mining. In: Krishna, C.R., Dutta, M., Kumar, R. (eds.) Proceedings of 2nd International Conference on Communication, Computing and Networking. LNNS, vol. 46, pp. 677–684. Springer, Singapore (2019). Scholar
  16. 16.
    Rokach, L., Maimon, O.Z.: Data Mining with Decision Trees: Theory and Applications. World Scientific (2008)Google Scholar
  17. 17.
    Singh, G.K., Jain, R.K., Dubey, P.: Study of classification techniques on medical datasets. In: Iyer, B., Nalbalwar, S.L., Pathak, N.P. (eds.) Computing, Communication and Signal Processing. AISC, vol. 810, pp. 557–565. Springer, Singapore (2019). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Aniket Muley
    • 1
    Email author
  • Parag Bhalchandra
    • 2
  • Govind Kulkarni
    • 2
  1. 1.School of Mathematical SciencesSRTM UniversityNandedIndia
  2. 2.School of Computational ScienceS.R.T.M. UniversityNandedIndia

Personalised recommendations