Skip to main content

Analyzing Student Performance Using Data Mining

  • Conference paper
  • First Online:
Ambient Communications and Computer Systems

Abstract

Analysis of student performance will help us understand the various factors that affect the overall of a student. Big Data Environment helps in analyzing the various concepts which are inbuilt for better strategies and the choices that are taken for an organization’s overall development. Reduction in cost, time, the development of optimized and novice products, efficient and smart decision-making are some of the fields where it proves to be useful. Considering, the Higher Education System, which is inculpated in predicting the performance of students, this work will help various institutions in not only enhancing the quality of education, but also upgrading the overall accomplishments, identifying the pupil’s at risk, and thereby refining the education resource management. This introspection will aid in identifying the patterns, where a comparative study between two distinct methods has been made in order to predict the student’s success and a database has been generated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. The journal for business education available from https://www.researchgate.net/publication/254344820_Academic_Performance_of_College_Students_Influence_of_Time_Spent_Studying_and_Working

  2. Oyelade, O.J., Oladipupo, O.O., Obagbuwa, I.C.: Application of K means clustering algorithm for prediction of student’s academic performance. J.-Int. J. Comput. Sci. Inf. Secur.

    Google Scholar 

  3. http://aishe.nic.in/aishe/home

  4. Roy, C., Rautaray, S.S., Pandey, M.: Big data optimization techniques: a survey. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 10(4):41–48 (2018). https://doi.org/10.5815/ijieeb.2018.04.06

  5. Osmanbegovic, E., Suljić, M.: Data mining approach for predicting student performance. Econom. Rev. J. Econom. Bus. X(1) (2012)

    Google Scholar 

  6. Kekane, S., Khairnar, D., Patil, R., Vispute, S.R., Gawande, N.: Automatic student performance analysis and automatic student performance analysis and monitoring monitoring. Int. J. Innovative Res. Comput. Commun. Eng. (2017)

    Google Scholar 

  7. Priya, I.U.: Predicting academic performance of students using data mining technique

    Google Scholar 

  8. Sa, C.L.: Dayang Hanani bt. Abang Ibrahim, Emmy Dahliana Hossain, Mohammad bin Hossin “Student Performance Analysis System (SPAS)”

    Google Scholar 

  9. Ramesh, V., Parkavi, P., Ramar, K.: Published a paper titled “Predicting Student Performance- A Statistical and Data Mining Approach. Int. J. Comput. Appl. (2013)

    Google Scholar 

  10. Suryadarma, D., Suryahadi, A., Sumarto, S., Rogers, F.H.: Improving student performance in public primary schools in developing countries: evidence from Indonesia. Available from http://www.tandfonline.com/loi/cede20

  11. Chemers, M.M., Hu, L., Garcia, B.F.: Academic self-efficacy and first-year college student performance and adjustment. Am. Psychol. Assoc.

    Google Scholar 

  12. García, Á.B.: Student Data Set

    Google Scholar 

  13. Das, N., et al.: Big data analytics for medical applications (2018)

    Google Scholar 

  14. Roy, C., Pandey, M., Rautaray, S.S.: A proposal for optimization of horizontal scaling in big data environment. Advances in Data and Information Sciences. Springer, Singapore, pp. 223–230 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pankhurhi Mallik .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mallik, P., Roy, C., Maheshwari, E., Pandey, M., Rautray, S. (2019). Analyzing Student Performance Using Data Mining. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5934-7_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5933-0

  • Online ISBN: 978-981-13-5934-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics