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DSS for Prognostication of Academic Intervention by Applying DM Technique

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Part of the Communications in Computer and Information Science book series (CCIS,volume 709)

Abstract

The decision making process in any field related to human behavior is an integral part of complex environment. Education has become a major concern of the development process in every field. The competent decision making to plan, execute and evaluate policies in this field became a necessity of this field which may be achieved by applying data mining techniques to educational environment. EDM uses many techniques such as decision trees, neural networks, k-nearest neighbor, naive bays, support vector machines and many others. The principal purpose of the study is identifying how each of these traditional processes can be improved through data mining techniques. And also to analyze the student behavior for future benefit by applying Data mining technique and using this valuable information for Decision Support to Prognostication of Academic Intervention for Higher Education using Data Mining Techniques. The study has suggested models and algorithms for every educational sphere we found while analyzing Education database. Data modeling processes is our main outcome, and enhanced processes achieved through data mining have been presented as research outcome. The techniques appropriate in achieving this enhanced processes has also been presented as modeling processes for finding the behavioral aspects and to effectively implement the organizational policies.

Keywords

  • DMT
  • Educational data mining
  • KDM
  • DPAI model

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References

  1. Al-Radaideh, Q.A., Al Ananbeh, A., Al-Shawakfa, E.M.: A classification model for predicting the suitable study track for school student’s. IJRRAS 8(2) (2011)

    Google Scholar 

  2. IBM SPSS Statistics 22 Documentation on internet. Accessed www.ibm.com/support/docview.wss?uid=swg27038407

  3. Cortez, P., Silva, A.: Using data mining to predict secondary school student performance. Accessed http://www.researchgate.net/publication/Using_data_mining_to_predict_secondary_school_student_performance

  4. Pritchard, M.E., Wilson, G.S.: Using emotional and social factors to predict student success. J. Coll. Student Dev. 44(1), 18–28 (2003)

    CrossRef  Google Scholar 

  5. Ali, S., et al.: Factors contributing to the students academic performance: a case study of Islamia University Sub-campus. Am. J. Edu. Res. 1(8), 283–289 (2013)

    CrossRef  Google Scholar 

  6. 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)

    CrossRef  Google Scholar 

  7. Bratti, M., Staffolani, S.: Student Time Allocation and Educational Production Functions. University of Ancona Department of Economics Working Paper No. 170. Ma, Y., Liu, B., Wong, C.K., Yu, P.S., Lee, S.M., Targeting the right (2000)

    Google Scholar 

  8. 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, Boulder, CO. IEEE (2003)

    Google Scholar 

  9. Kotsiantis, S.: A case study for predicting dropout prone students. Int. J. Knowl. Eng. Soft Data Paradigms 1(2), 101–111 (2009)

    CrossRef  Google Scholar 

  10. 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)

    CrossRef  Google Scholar 

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Correspondence to S. D. Khamitkar , P. U. Bhalchandra , S. N. Lokhande , Preetam Tamsekar , Govind Kulkarni or Kailas Hambarde .

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Wasnik, P. et al. (2017). DSS for Prognostication of Academic Intervention by Applying DM Technique. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_18

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  • DOI: https://doi.org/10.1007/978-981-10-4859-3_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4858-6

  • Online ISBN: 978-981-10-4859-3

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