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Educational Data Mining Survey for Predicting Student’s Academic Performance

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Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018) (ICCBI 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 31))

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Abstract

In Era of 21st Century, Competition in Education Field is becoming major topic to be discussed and should be focused by the teacher as student’s career decision is depending on their major discipline they are studying. It is the responsibility to judge the students and help them to develop their career path. Data Mining is the most suitable technique to analyze the student’s performance. Lots of work is already done in this direction, but still there are many parameters to be considered. This paper presents the survey on Educational Data Mining. Also presents the finding of that research on student performance. Provided information is very helpful for the research scholar to get existing methods for EDM.

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References

  1. Saranya, S., Ayyappan, R., Kumar, N.: Student progress analysis and educational institutional growth prognosis using data mining. Int. J. Eng. Sci. Res. Technol. (2014)

    Google Scholar 

  2. Sembiring, S.: Prediction of student academic performance by an application of data mining. In: International Conference on Management and Artificial Intelligence (2011)

    Google Scholar 

  3. Waiyamai, K.: Improving Quality of Graduate Students by Data Mining. Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand (2003)

    Google Scholar 

  4. Archer, E.: Benchmarking the habits and behaviours of successful students: a case study of academic-business collaboration. Int. Rev. Res. Open Distance Learn. 15(1) (2014)

    Google Scholar 

  5. Kabakchieva, D.: Student performance prediction by using data mining classification algorithms. Int. J. Comput. Sci. Manag. Res. 1(4), 686–690 (2012)

    Google Scholar 

  6. Luan, J.: Data mining applications in higher education. SPSS Inc. (2004)

    Google Scholar 

  7. Kotsiantis, S.P.: Prediction of student’s performance in distance learning using machine learning techniques. Appl. Artif. Intell. 18(5), 411–426 (2004)

    Article  Google Scholar 

  8. Kavacic, Z.J.: Early prediction of student success: mining students enrolment data. In: Proceedings of Informing Science and IT Education Conference, pp. 647–665 (2010)

    Google Scholar 

  9. Cairns, A.H., Gueni, B., Fhima, M., David, S., Khelifa, N.: Towards custom designed professional training contents and curriculums through educational process mining. In: The Fourth International Conference on Advances in Information Mining and Management (2014)

    Google Scholar 

  10. Badal, D., Arora, R.: Mining association rules to improve academic performance. Int. J. Comput. Sci. Mobile Comput. 3(1), 428–433 (2014)

    Google Scholar 

  11. Osmanbegovic, E., Suljic, M.: Data mining approach for predicting student performance. Econ. Rev. 10(1) (2012)

    Google Scholar 

  12. El-Halees, A.M.: Mining student data to analyze learning behaviour: a case study. Department of Computer Engineering, Islam University, Kushtia (2009)

    Google Scholar 

  13. Kotsiantis, P.: Predicting students’ marks in Hellenic Open University. In: Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05) (2005)

    Google Scholar 

  14. Suknya, M., Birutha, S.: Data mining: performance improvement in education sector using classification and clustering algorithm. In: International Conference on Computing and Control Engineering, vol. 12 (2012)

    Google Scholar 

  15. Ying, Z., Qussena, S.: Use data mining to improve student retention in higher education—a case study. In: ICEIS 2010 - Proceedings of the 12th International Conference on Enterprise Information Systems, vol. 1, Portugal, 8–12 June 2008 (2010)

    Google Scholar 

  16. Jansen, E.P.W.A., Torenbeek, M., Hofman, W.H.A.: Predicting first-year achievement by pedagogy and skill development in the first weeks at university. Teach. High. Educ. 16, 655–668 (2011)

    Article  Google Scholar 

  17. Hassel, S., Ridout, N.: An investigation of first-year students’ and lecturers expectations of university education. Front. Psychol. (2018)

    Google Scholar 

  18. HEFCE England.: Year one outcomes for first degree students. In: Heads of HEFCE-Funded Higher Education Institutions (2017)

    Google Scholar 

  19. Fraser, W.J., Killen, R.: Factors influencing academic success or failure of first-year and senior university students: do education students and lecturers perceive things differently? S. Afr. J. Educ. 23, 254–263 (2003)

    Google Scholar 

  20. He, Y.Q.: Application of data mining on students’ quality evaluation. In: 3rd International Workshop on Intelligent Systems and Applications. IEEE (2011)

    Google Scholar 

  21. Sakurai, Y.: Success chances estimation of university curricula based on educational history, self-estimated intellectual traits and vocational ambitions. In: 11th IEEE International Conference, Advanced Learning Technologies (ICALT) (2011)

    Google Scholar 

  22. Aher, S.B.: Data mining in educational system using weka. In: IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT), vol. 3 (2011)

    Google Scholar 

  23. Sheela, A., Tasleem, A.M., Ahsan, M.: Data mining model for higher education system. Eur. J. Sci. Res. 43, 24–29 (2010)

    Google Scholar 

  24. Kovacic, Z.: Early prediction of student success: mining students’ enrolment data. In: Informing Science & IT Education Conference (InSITE) (2010)

    Google Scholar 

  25. Penchenizkiy, M., Dekker, G.W.: Predicting student drop out: a case study. In: Proceedings of the 2nd International Conference on Educational Data Mining (EDM’09), pp. 41–50 (2009)

    Google Scholar 

  26. Asma, A., Nusari, A.N., Al-Shargabi, AA.: Discovering vital patterns from UST students data by applying data mining techniques. In: IEEE The 2nd International Conference Computer and Automation Engineering (ICCAE), vol. 2 (2010)

    Google Scholar 

  27. Ogar, E.N.: Student academic performance monitoring and evaluation using data mining techniques. In: Fourth Congress of Electronics and Automotive Mechanics, pp. 354–359 (2007)

    Google Scholar 

  28. Yan, Z.M., Shenm, Q.: The analysis of student’s grade based on rough sets. In: 3rd IEEE International Conference, Ubi-Media Computing (U-Media) (2010)

    Google Scholar 

  29. Duan, Q., Yang, Y.L., Li, Y.: Rough K-modes clustering algorithm based on entropy. IAENG Int. J. Comput. Sci. 44, 13–18 (2017)

    Google Scholar 

  30. Sakurai, Y., Knauf, R.: Empirical evaluation of a data mining method for success chance estimation of university curricula. In: IEEE International Conference in Systems Man and Cybernetics (SMC), pp. 1127–1133. IEEE (2010)

    Google Scholar 

  31. Wu, Z., Zhag, H.: Study of comprehensive evaluation method of undergraduates based on data mining. In: IEEE International Conference in Intelligent Computing and Integrated Systems (ICISS), pp. 541–543 (2010)

    Google Scholar 

  32. Liu, Z., Zhang, X.: The application of data mining technology in analysis of college student’s performance Information Science (2010)

    Google Scholar 

  33. Lijuan, Y.C.: Improved Data Mining Algorithm Based on an Early Warning System of a College Students. Academy Publisher, Chicago (2013)

    Google Scholar 

  34. Wang, P.-J., Shi, L., Bai, J.-N., Zhao Y.-L.: Mining association rules based on apriori algorithm and application. In: IEEE International Forum, In Computer Science-Technology and Applications, vol. 1, pp. 141–143 (2009)

    Google Scholar 

  35. Ramasubramanian, P., Iyakutti, P.: Enhanced data mining analysis in higher educational system using rough set theory. Afr. J. Math. Comput. Sci. Res. 2, 184–188 (2009)

    Google Scholar 

  36. Ring, Z., Shangping, D.: A data mining algorithm in distance learning. In: 12th International Conference on Computer Supported Cooperative Work in Design. IEEE (2008)

    Google Scholar 

  37. Punch, W.F., Minai Bidgoli, B.: Using genetic algorithm for data mining optimization in an educational web system (2009). http://www.lon-copa.org

  38. Bresfelean, V.P., Bresfelean, M., Ghisoiu, N.: Determining students’ academic failure profile founded on data mining methods. In: IEEE 30th International Conference on Information Technology Interfaces, pp. 317–322 (2008)

    Google Scholar 

  39. Bresfelean, V.P.: Analysis and predictions on students’ behavior using decision trees in Weka environment. In: IEEE 29th International Conference on Information Technology Interfaces (2007)

    Google Scholar 

  40. Velmurugan, T., Anandavally, R., Anuradha, C.: Clustering algorithm in educational data mining. Int. J. Power Control Comput. (IJPCSC) 7(1), 47–52 (2015)

    Google Scholar 

  41. Vijitha, C., Durairaj, M.: Educational data mining for prediction of students performance using clustering algorithms. Int. J. Comput. Sci. Inf. Technol. 5, 5987–5991 (2014)

    Google Scholar 

  42. Garg, R., Rana, S.: Application of hierarchical clustering algorithm to evaluate students performance of an institute. In: 2nd International Conference Computational intelligence and Communication Technology (2016)

    Google Scholar 

  43. Kadijala, S., Potturi, C.S.: Analyzing the student’s academic performance by using clustering methods in data mining. Int. J. Sci. Eng. Res. 5(6), 198–202 (2014)

    Google Scholar 

  44. Periyasamy, R., Veeramuthu, P.: Analysis of student result using clustering techniques. Int. J. Comput. Sci. Inf. Technol. 5(4), 5092–5094 (2015)

    Google Scholar 

  45. Zhang, X., Liu, G.: Score data analysis for pre-warning students in university. In: IEEE 4th International Conference on Wireless Communications, Networking and Mobile Computing (2008)

    Google Scholar 

  46. Calvo, R.A., Villalon, J.: Concept map mining: a definition and a framework for its evaluation. In: IEEE International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3 (2008)

    Google Scholar 

  47. Hien, N.T.N., Haddawy, P.: A decision support system for evaluating international student applications. In: 37th Annual Frontiers in Education Conference IEEE Global Engineering: Knowledge Without Borders, Opportunities Without Passports (2007)

    Google Scholar 

  48. Al-Radaideh, Al-Shawakfa, E.M., Al-Najjar, M.I.: Mining student data using decision trees. In: International Arab Conference on Information Technology, Jordan (2006)

    Google Scholar 

  49. Sheb, Q., Kairul, A.: Subsethood based fuzzy rule models and their application to student performance classification. In: The 14th IEEE International Conference on Fuzzy Systems. IEEE (2005)

    Google Scholar 

  50. Janeek, P., Thai, N., Haddawy, P.: A comparative analysis of techniques for predicting academic performance. In: Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports. IEEE (2007)

    Google Scholar 

  51. Ventura, S., Romero, C.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007)

    Article  Google Scholar 

  52. Prasong, P., Pumpuang, P., Srivinok, A.: Comparisons of classifier algorithms: Bayesian network, C4. 5, decision forest and NBTree for course registration planning model of undergraduate students. In: IEEE International Conference on Systems, Man and Cybernetics (2008)

    Google Scholar 

  53. Murray, M.: Factors affecting graduation and student dropout rates at the University of KwaZulu-Natal. S. Afr. J. Sci. 110(11/12) (2014)

    Google Scholar 

  54. Suljic, M., Agic, H., Osmanbegovic, E.: Determining dominant factor for students performance prediction by using data mining classsification algorithm. In: Tuzla (2014)

    Google Scholar 

  55. Rahman, A., Dash, S.: Data mining for student’s tend analysis using Apriori algorithm. Int. J. Control Theory Appl. 10, 18 (2017)

    Google Scholar 

  56. Singh, S.P., Malik, S., Singh, P.: Research paper factors affecting academic performance of students. PARIPEX Indian J. Res. 5(4), 176–178 (2016)

    Google Scholar 

  57. Bonde, S.N., Kirange, D.K.: Survey on evaluation of student’s performance in educational data mining. In: IEEE 2nd International Conference on Inventive Communication and Computational Technologies, pp. 209–213 (2018)

    Google Scholar 

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Acknowledgement

Author thanks all colleagues who provided insight and expertise that greatly assisted the given research, also thanks to guide for assistance with JTM COE, Faizpur for comments that greatly improved the manuscript. Author also Thanking to KBCNMU Jalgaon for providing us facilities as requirements.

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Authors and Affiliations

  1. KBCNMU University, Jalgaon, India

    Sharayu N. Bonde

  2. JTMGCOE, Faizpur, India

    D. K. Kirange

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  1. Sharayu N. Bonde
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  2. D. K. Kirange
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Correspondence to Sharayu N. Bonde .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, India

    A. Pasumpon Pandian

  2. Department of Engineering, University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

  4. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Bonde, S.N., Kirange, D.K. (2020). Educational Data Mining Survey for Predicting Student’s Academic Performance. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_35

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  • DOI: https://doi.org/10.1007/978-3-030-24643-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24642-6

  • Online ISBN: 978-3-030-24643-3

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