UIJRT » United International Journal for Research & Technology

A Systematic Review on the Educational Data Mining and its Implementation in the Applications

Total Views / Downloads: 71 

Cite ➜

Kumar, S.S., 2020. A Systematic Review on the Educational Data Mining and its Implementation in the Applications. United International Journal for Research & Technology (UIJRT), 1(8), pp.01-03.


The Mining of the education data is emerging trend in the learning analytics as it is time consuming to analyses the data and to identify the hidden information automatically. In this paper, detailed investigation about the educational data mining technique is carried out. The Application of the data mining includes storing and retrieval of the student data in the large repositories such as mark sheet, attendance sheet and student profile etc. The importance analysis is carried out on the retrieval of the large data using machine learning algorithm in the data mining. Along the retrieval of the data, nowadays deep focus is made on predicting and recommendation models which provide more effectiveness to the educational applications in terms of suggestions and extracting the correlation among the students. However, handling of large data from repositories leads to performance bottleneck, hence it is resolved by employing Map Reduce Paradigm from big data analytics. Through extensive study, classification and clustering provides more value for the data management, hence semantic and opinion mining is presented as the future research solution.

Keywords: Educational data mining, Clustering, Classification, Data Prediction, Recommendation, Map Reduce, Semantic and Opinion Mining.


  1. J. Vandamme, -P.,Meskens, N., Superby, F.-, J, “Predicting Academic Performance by Data Mining Methods,” Education Economics, vol. 15, pp. 405-419, 2007.
  2. R. S. Baker and K. Yacef, “The state of educational data mining in 2009: A review and future visions,” JEDM-Journal of Educational Data Mining, 2009.
  3. O. R. Zaıane, “Building a recommender agent for e-learning systems,” in Computers in Education, 2002. Proceedings. International Conference on, 2002, pp. 55-59.
  4. T. S. Madhulatha, “An overview on clustering methods,” arXiv preprint arXiv:1205.1117, 2012.
  5. J. Manyika, M. Chui, B. Brown, and J. Bughin, “Big data: The next frontier for innovation, competition, and productivity,” 2011.
  6. S. Parack, Z. Zahid, and F. Merchant, “Application of data mining in educational databases for predicting academic trends and patterns,” in Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on, 2012, pp. 1-4.
  7. Leena Khanna et.al “Educational data mining and its role in determining factors affecting students academic performance: A systematic review” in Information Processing (IICIP), 2016 1st India International Conference.
  8. Jorge Luis Cavalcanti Ramos et.al “A Comparative Study between Clustering Methods in Educational Data Mining”in IEEE Latin America Transactions vol.14, issue ;8, Aug 2016.
  9. Carlos Marquez-Vera et.al “Predicting School Failure and Dropout by Using Data Mining Techniques” in IEEE Latin American journal of learning technologies, vol: 8, issue: 1, Feb2013.
  10. Antonio Garrido et.al ” E-Learning and Intelligent Planning: Improving Content Personalization” in IEEE Ibero-American Review of Learning Technologies, Vol: 9, issue: 1, feb 2014.

For Conference & Paper Publication​

UIJRT Publication - International Journal