Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery· 2012Q1
Data mining in education
- 790citations
- Q1SCImago
- 2012year
Short summary
Educational Data Mining (EDM) adapts traditional data mining techniques to analyze unique educational data, aiming to improve student learning and educational outcomes.
AI-generated from the title and abstract; the full text is not read.
Key points
- Educational Data Mining (EDM) is an interdisciplinary field focused on analyzing data from educational settings.
- The goal of EDM is to improve student learning and educational outcomes by understanding how and where students learn.
- Traditional data mining techniques often need modification to handle the unique data types and problem specificities in education.
- The paper provides a review of key milestones, current applications, tools, and future insights in EDM.
AI-generated from the title and abstract; the full text is not read.
Abstract
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
The authors' abstract, as published at the source. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 2012 · DOI ↗
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Field: Computer Science Applications
Computer Science ApplicationsComputer Science