Regression Model for Edu-data in Technical Education System: A Linear Approach

  • P. K. Srimani
  • Malini M. Patil
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Mining educational data is an emerging interdisciplinary research area that mainly deals with the development of methods to explore the data stored in educational institutions which is referred to as Edu-Data. Data mining is concerned with the analysis of data for finding patterns which are previously unknown and are presently useful for future analysis. The technique of mining Edu-data is referred to as Edu-mining. On the other hand statistics is a mathematical science concerned with the collection, analysis, interpretation or explanation, and presentation of data which plays a very important role in the process of data mining. The paper aims at developing a simple linear regression model for Edu-data using the statistical approach. The results obtained helps the management to predict the semester results and also helps in proper decision making processes in Technical Education System. It is also found that the predictions were almost nearing to the actual values. The present work is first of its kind in literature.


Edu-data Edu-mining Data Mining Regression Prediction Visualization 


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  1. 1.
    Srimani, P.K., Patil, M.M.: Edu-mining: A Machine learning approach. In: AIP. Conf. Proceedings, pp. 61–66 (2011)Google Scholar
  2. 2.
    Srimani, P.K., Patil, M.M.: A Classification Model for Edu-mining. In: PSRC-ICICS Conference Proceedings, pp. 35–40 (2012)Google Scholar
  3. 3.
    Srimani, P.K., Patil, M.M.: A Comparative Study of Classifiers for Student Module IN Technical Education System(TES). International Journal of Current Research 4(01), 249–254 (2012)Google Scholar
  4. 4.
    Srimani, P.K., Patil, M.M., Srivatsa, P.K.: Performance evaluation of Classifiers for Edu-data: An integrated approach. International Journal of Current Research 4(02), 183–190 (2012)Google Scholar
  5. 5.
    Feng, M., Heffernan, N.: Informing teachers live about student learning: Reporting in the assessment system. Technol., Instruction, Cognition, Learn. J. 3, 1–8 (2006)Google Scholar
  6. 6.
    Freedman, D., Purves, R.: Statistics 4th edn., NewyorkGoogle Scholar
  7. 7.
    Zinn, C., Scheuer, O.: Getting to know your students in Distance learning contexts. In: Proc. 1st Eur. Conf. Technol. Enhanced Learn., pp. 437–451 (2006)Google Scholar
  8. 8.
    Mazza, R.: Introduction to Information Visualization. Springer, NewYork (2009)Google Scholar
  9. 9.
    Han, J., Kambler, M.: Data Mining Concepts and Techniques, 2nd edn. Morgan Kaufmann (2007)Google Scholar
  10. 10.
    PASW Statistics 18 Brief Guide. Prentice Hall (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.R&DBangalore UniversityBangaloreIndia
  2. 2.Dept. of ISEJSSATEBangaloreIndia
  3. 3.Bhartiyaar UniversityCoimbatoreIndia

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