Educational data mining: A literature review

With the aim of disseminating the potential and the capacity of Educational Data Mining (EDM) as an instrument of investigation and analysis in the support to the management of Higher Education Institutions, this paper presents a brief description of some of the most relevant studies in the area. The analysis carried out allows to highlight the innovations that EDM has been promoting, as well as current and future research trends.

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[9]  Patricia Omega Kukoyi,et al.  Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques , 2016 .

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[13]  George Siemens.  Connectivism: A Learning Theory for the Digital Age , 2004 .

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[15]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[16]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[17]  Alejandro Peña-Ayala,et al.  Educational data mining , 2014 .

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[19]  Hana Bydzovská,et al.  A Comparative Analysis of Techniques for Predicting Student Performance , 2016, EDM.

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[21]  Dursun Delen,et al.  A comparative analysis of machine learning techniques for student retention management , 2010, Decis. Support Syst..

[22]  Moti Zwilling,et al.  Student data mining solution-knowledge management system related to higher education institutions , 2014, Expert Syst. Appl..

[23]  Wahidah Husain,et al.  A Review on Predicting Student's Performance Using Data Mining Techniques , 2015 .

[24]  Hong Liu,et al.  Use Educational Data Mining to Predict Undergraduate Retention , 2016, 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT).

[25]  Hongjie Sun,et al.  Research on Student Learning Result System based on Data Mining , 2010 .

[26]  Antonio García-Cabot,et al.  Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance , 2016, Comput. Hum. Behav..

[27]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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[30]  Habib Fardoun,et al.  Early dropout prediction using data mining: a case study with high school students , 2016, Expert Syst. J. Knowl. Eng..

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[33]  Tim Menzies,et al.  Learning patterns of university student retention , 2011, Expert Syst. Appl..

[34]  Zoran Popovic,et al.  Learning Individual Behavior in an Educational Game: A Data-Driven Approach , 2014, EDM.

[35]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

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[37]  Ryan Shaun Joazeiro de Baker,et al.  Educational Data Mining: An Advance for Intelligent Systems in Education , 2014, IEEE Intelligent Systems.

[38]  Naveen Aggarwal,et al.  The recent state of educational data mining: A survey and future visions , 2015, 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE).

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[42]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[43]  Vassilis Loumos,et al.  Dropout prediction in e-learning courses through the combination of machine learning techniques , 2009, Comput. Educ..

[44]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[45]  Paulo Cortez,et al.  Using data mining to predict secondary school student performance , 2008 .

[46]  Bindiya M. Varghese,et al.  Clustering Student Data to Characterize Performance Patterns , 2011 .

[47]  Aditya Johri,et al.  Next-Term Student Performance Prediction: A Recommender Systems Approach , 2016, EDM.

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