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Data mining in education

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Abstract Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Business and Industry

Main areas related to educational data mining.

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Example of nonnegative matrix factorization and Q‐matrix interpretation.

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Different levels of granularity and their relationship to the amount of data.

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Educational knowledge discovery and data mining process.

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Types of traditional and computer‐based educational environments and systems.

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Number of educational data mining references in Google Schoolar and cites in SciVerse Scopus by year.

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