Abstract
Educational Data Mining (EDM) is a dawning interdisciplinary research field that concerns with the development of tools/methods to analyze enormous amount of data generated by or related to an educational framework or system. Computational approaches may be employed to explore the educational data and study the educational queries. This paper surveys the important studies/debates carried out in EDM. It talks about the various components that form a part of the EDM system, and lists the goals of EDM. Firstly, it identifies the different tasks that can be applied in educational environment. It then provides the most common tasks/problems in the educational system that have been solved through data mining (DM) techniques. It also compares the different techniques employed in terms of the merits and demerits.
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References
Baker R (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds.) International encyclopedia of education, 3rd edn. Oxford, UK Elsevier
Romero C, Ventura S (2010) Educational data mining: a review of the state-of-the-art. IEEE Trans Syst Man Cybern Part C Appl Rev 40(6):601–618
“http://educationaldatamining.org/EDM2008/. Accessed 04 Sept 2013
Baker RS, Yacef K (2009) The state of educational data mining in 2009: a review and future visions. JEDM-J. Educ Data Min 1(1):2017
Castro F, Vellido A, Nebot A, Mugica F (2007) Applying data mining techniques to e-learning problems. In: Jain LC, Tedman R, Tedman D (eds.) Evolution of teaching and learning paradigms in intelligent environment. studies in computational intelligence, vol 62, Springer, pp 183–221
Cristóbal R, Sebastian V (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33:135–146. https://doi.org/10.1016/j.eswa.2006.04.005
Draper NR, Smith H (1998) Applied regression analysis. Wiley, Hoboken
Phyu TN (2009) Survey of classification techniques in data mining. In: International multi conference of engineers and computer scientists, Hong Kong, pp 1–5
Haddawy P, Thi N, Hien TN (2007) A decision support system for evaluating international student applications. In: Frontiers in education conference, Milwaukee, pp 1–4
Mcdonald B (2004) Predicting student success. J Math Teach Learn 1–14
Psaromiligkos Y, Orfanidou M, Kytagias C, Zafiri E (2009) Mining log data for the analysis of learners’ behaviour in web-based learning management systems. Oper Res J 1–14
Sheard J, Ceddia J, Hurst J, Tuovinen J (2003) Inferring student learning behaviour from website interactions: a usage analysis. J Educ Inf Technol 8(3):245–266
Ayers E, Nugent R, Dean N (2009) A comparison of student skill knowledge estimates. In: International conference on educational data mining, Cordoba, Spain, pp 1–10
Superby JF, Vandamme JP, Meskens N (2006) Determination of factors influencing the achievement of the first-year university students using data mining methods. In: International conference on intelligent tutoring systems, educational data mining workshop, Taiwan, pp 1–8
Kelly D, Tangney B (2005) First aid for you: getting to know your learning style using machine learning. In: IEEE international conference on advanced learning technologies, Washington, DC, pp 1–3
Crespo RM, Pardo A, Pérez JP, Kloos CD (2005) An algorithm for peer review matching using student profiles based on fuzzy classification and genetic algorithms. In: International conference on innovations in applied artificial intelligence, Bari, Italy, pp 685–694
Zhang L, Liu X, Liu X (2008b). Personalized instructing recommendation system based on web mining. In: International conference for young computer scientists, Hunan, China, pp 2517–2521
Zaïane O (2002) Building a recommender agent for e-learning systems. In: Proceedings of the international conference in education, Auckland, New Zealand, pp 55–59
Herlocker J, Konstan J, Tervin LG, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst J 22(1):5–53
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Anjum, N., Badugu, S. (2020). A Study of Different Techniques in Educational Data Mining. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_65
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