Educational data mining and learning analytics: An updated survey

Abstract

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

References in corpus (3)

• Text mining in education
• A holographic mobile-based application for practicing pronunciation of basic English vocabulary for Spanish speaking children
• Mining Educational Data to Analyze Students' Performance

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• Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering
• A review of clustering models in educational data science towards fairness-aware learning
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• Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning
• Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool
• A Systematic Review on Process Mining for Curricular Analysis
• AIED's Unfinished Mission: Centering Agency and Motivation in the Age of Effortless Bypass
• Predicting students' performance in online courses using multiple data sources