Skip to main content
SpringerLink
Log in

Predicting Students Performance in Examination Using Supervised Data Mining Techniques

  • Conference paper
  • First Online:
Informatics and Intelligent Applications (ICIIA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1547))

Included in the following conference series:

Abstract

There are challenges in evaluating and predicting student learning outcomes because they are based on multiple factors. Predicting the performance of students in the exam is very important for identifying capable students or not too good students and its sole purpose is to identify students who may need extra assistance before the examination is conducted. Several researchers have worked on these challenges and so far no comprehensive research has been conducted to compare in detail the performance of machine learning techniques related to predicting student performance. This study proposes data extraction techniques decision tree (DT) and K-Nearest Neighbour (KNN) for the prediction of the student’s performance in the exam. The article then compare the result of the two techniques to recommend the best. The study shows that Decision Tree DT for predicting pass/fail status of students in an academic course delivers the most successful outcomes, giving 91% success rate. The model would aid the professor in taking the required measures to assist students with issues in their courses, which generally result in a course being repeated.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Osmanbegović, E., Agić, H., Suljić, M.: Prediction of students’ success by applying data mining algorithams. J. Theor. Appl. Inf. Technol. 61(2), 378–388 (2014)

    Google Scholar 

  2. Abayomi-Alli, A., Misra, S., Fernández-Sanz, L., Abayomi-Alli, O., Edun, A.R.: Genetic algorithm and tabu search memory with course sandwiching (GATS_CS) for university examination timetabling. Intell. Autom. Soft Comput. 26(3), 385–396 (2020)

    Article  Google Scholar 

  3. Dietz-Uhler, B., Hurn, J.E.: Using learning analytics to predict (and improve) student success: a faculty perspective. J. Interact. Online Learn. 12(1), 17–26 (2013)

    Google Scholar 

  4. Avella, J.T., Kebritchi, M., Nunn, S.G., Kanai, T.: Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learn. 20(2), 13–29 (2016)

    Google Scholar 

  5. Kay, D., Korn, N., Oppenheim, C.: Legal, risk and ethical aspects of analytics in higher education. Analytics series (2012)

    Google Scholar 

  6. Oladipo, I., et al.: An improved course recommendation system based on historical grade data using logistic regression. In: Florez, H., Pollo-Cattaneo, M.F. (eds.) ICAI 2021. CCIS, vol. 1455, pp. 207–221. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89654-6_15

    Chapter  Google Scholar 

  7. Figueroa-Canas, J., Sancho-Vinuesa, T.: Early prediction of dropout and final exam performance in an online statistics course. Revista Iberoamericana de Tecnologias del Aprendizaje 15(2), 86–94 (2020). https://doi.org/10.1109/RITA.2020.2987727

    Article  Google Scholar 

  8. Wu, Z., et al.: Exam paper generation based on performance prediction of student group. Inf. Sci. 532, 72–90 (2020). https://doi.org/10.1016/j.ins.2020.04.043

    Article  Google Scholar 

  9. Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., Rangwala, H.: Predicting student performance using personalized analytics. Computer 49(4), 61–69 (2016)

    Article  Google Scholar 

  10. Christina, M.: Predicting student performance using data mining. Int. J. Comput. Sci. Eng. 6(10), 172–177 (2018). https://doi.org/10.26438/ijcse/v6i10.172177

    Article  Google Scholar 

  11. Adewumi, A., Adia, F., Misra, S.: Design and implementation of an online examination system for grading objective and essay-type questions. Int. J. Control Theor. Appl 9(23), 363–370 (2016)

    Google Scholar 

  12. Ahmed, A.B.E.D., Elaraby, I.S.: Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)

    Google Scholar 

  13. Kabakchieva, D., Stefanova, K., Kisimov, V.: ‘Analyzing university data for determining student profiles and predicting’, in Performance. In: Conference Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011), pp. 347–48 (2011)

    Google Scholar 

  14. Tanner, T., Toivonen, H.: Predicting and preventing student failure – using the k-nearest neighbour method to predict student performance in an online course environment. Int. J. Learn. Technol. 5(4), 356 (2010). https://doi.org/10.1504/ijlt.2010.038772

    Article  Google Scholar 

  15. Kumar, T.R., Vamsidhar, T., Harika, B., Kumar, T.M., Nissy, R.: Students performance prediction using data mining techniques. In: 2019 International Conference on Intelligent Sustainable Systems (ICISS), pp. 407–411. IEEE, February 2019

    Google Scholar 

  16. Damaševičius, R.: Analysis of academic results for informatics course improvement using association rule mining. In: Papadopoulos, G., Wojtkowski, W., Wojtkowski, G., Wrycza, S., Zupancic, J. (eds.) Information Systems Development, pp. 357–363. Springer, Boston (2009). https://doi.org/10.1007/b137171_37

  17. Adewumi, A., Obinnaya, L., Misra, S.: Design and implementation of a mobile based timetable filtering system. Int. J. Control Theory Appl. 9(23), 371–375 (2016)

    Google Scholar 

  18. bin Mohd Nasir, M., bin Asmuni, M., Salleh, N., Misra, S.: A review of student attendance system using near-field communication (NFC) technology. In: Gervasi, O., et al. (eds.) ICCSA 2015. LNCS, vol. 9158, pp. 738–749. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21410-8_56

    Chapter  Google Scholar 

  19. Abisoye, O.A., Akanji, O.S., Abisoye, B.O., Awotunde, J.: Slow hypertext transfer protocol mitigation model in software defined networks. In: 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1–5. IEEE, October 2020

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Kazeem Moses Abiodun, Emmanuel Abidemi Adeniyi & Emmanuel Ogbuji

  2. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Dayo Reuben Aremu & Joseph Bamidele Awotunde

  3. Life on Land Research Group, Landmark University SDG 15, Omu-Aran, Nigeria

    Kazeem Moses Abiodun & Emmanuel Ogbuji

  4. Quality Education Group, Landmark University SDG 4, Omu-Aran, Nigeria

    Kazeem Moses Abiodun, Emmanuel Abidemi Adeniyi & Emmanuel Ogbuji

  5. Industry, Innovation and Infrastructure Research Group, Landmark University SDG 9, Omu-Aran, Nigeria

    Emmanuel Abidemi Adeniyi

Authors
  1. Kazeem Moses Abiodun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Abidemi Adeniyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dayo Reuben Aremu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph Bamidele Awotunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emmanuel Ogbuji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joseph Bamidele Awotunde .

Editor information

Editors and Affiliations

  1. Østfold University College, Halden, Norway

    Sanjay Misra

  2. Covenant University, Ota, Nigeria

    Jonathan Oluranti

  3. Silesian University of Technology, Gliwice, Poland

    Robertas Damaševičius

  4. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abiodun, K.M., Adeniyi, E.A., Aremu, D.R., Awotunde, J.B., Ogbuji, E. (2022). Predicting Students Performance in Examination Using Supervised Data Mining Techniques. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-95630-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95629-5

  • Online ISBN: 978-3-030-95630-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation