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Impact of Data Mining Technology on the Quality of Higher Education Institutions

Affiliations

  • Assistant Professor, BYK College of Commerce, Nashik, SPPU, India
  • Associate Professor, DVVPF’s, IBMRD, Ahmednagar, SPPU, India

Abstract


Higher Education Institutions (HEI) are increasing rapidly because of the increasing demand of the society. The primary goal of Higher Education Institution is to impart quality education to student and to develop skillset of the students for their sustainable development. To survive in the competition, educational institutions have to establish perfect education management system. The recent trend in higher education sector known as data mining technology, helps higher education institutions for their sustainable growth. Data mining technology helps to identify new patterns and trends from huge educational data sets. Implementation of data mining techniques in the field of education is called as Educational Data Mining (EDM). This paper illustrates the role of various data mining techniques such as clustering, association rule, prediction, classification etc. for improving the quality of higher education institutions.

Keywords

Higher Education Institution, Data mining technology, Educational Data Mining, Data mining algorithms etc.

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References


  • Annaisa and Harwati (2017), “A Comparative study using student’s performance using Educational Data Mining Techniques.”, IOP Conference Series 215

  • A Harika, Akshatha K A, Anirudh A, Eesha B S, Prof. Sandhya A Kulkarni (2022), “Student Marks Prediction Using Machine Learning Techniques”, International Journal of creative Research thoughts, Volume 10, Issue 7 July 2022 | ISSN: 2320-2882

  • Arcinas, Guna Sekhar Sajja, Shazia Asif, Sanjeev Gour, Ethelbert Okoronkwo, Mohd Naved (2021), “Role of Data Mining in Education for improving students’ performance for Social Change”, Turkish Journal of Physiotherapy and rehabilitation 32(3):6519

  • Boumedyen Shannaq, Yusupov Rafael, V. Alexandro, “Student Relationship in Higher Education using Data Mining Techniques”, Global Journal of Computer Science and Technology, Vol 10, Issue 11, (Ver. 1.0), October 2010, Page-54.

  • Eyman Alyahyan & Dilek Dustegor, “Predicting Academic Success in Higher Education: Literature Review and Best Practices”, International Journal of Education Technology in Higher Education, 17, Article No. 3, 2020.

  • Haitham Alagib Alsuddig Hamza1 Piet Kommers, “A Review of Educational Data Mining Tools and Techniques”, International Journal of Educational Technology and Learning ISSN: 2523-0581 Vol. 3, No. 1, pp. 17-23, 2018 DOI: 10.20448/2003.31.17.23

  • Hanan Abdullah Mengash (2020), “Using Data Mining Techniques to Predict Student performance to Support Decision Making in University Admission Systems”, IEEE Access Volume-8, February 14, 2020

  • Jaya Srivastava and Dr. Abhaykumar Shrivastava, “Data Mining in Education Sector:

  • AReview”, Special Conference Issue 2020, National Conference on Cloud Computing and Big Data, Pages 184-190

  • P. Bachhal, S. Ahuja1 and S. Gargrish (2021), “Educational Data Mining - A Review”, Journal of Physics: Conference Series 1950(2021) 012022

  • Rajeshwari A. M., M. Sridevi, C. Deisy Chelliah, “Outliers Detection on Educational Data using Fuzzy Association Rule Mining”, Int.Conf. on Adv. In Comp., Comm, and Inf. Sci.(ACCIS-14) (1-9)

  • Richard A. Huebner, “A survey of Educational Data Mining Research”, Research in Higher Education Journal

  • Sushil Verma, R. S. Thakur, Shailesh Jalori, “A Study of the Applications of Data Mining Techniques in Higher Education”, International Journal of Computer and Communication Technology, Volume-6, Issue-1, January 2015.

  • Suwarna Mulay, Dr. Shubhangi Potdar, “Data Mining Techniques to Predict Student Academic Performance in Higher Education: Literature Review”, Journal of Advanced Zoology, Vol. 45 No. S4(2024), 167-173


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