Educational data mining and learning analytics: An updated survey
By Cristóbal Romero, Sebastián Ventura. WIREs Data Mining Knowl. Discov., 2020
960 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Computer Science Applications, Health Information Management
View Paper: https://www.semanticscholar.org/paper/7bd598f6a7c6eb4265fe5a9ca64504d1d639684a
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In Computer Networks and Communications
In Computer Science Applications
Citations in the corpus
- Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, background): “Although the number of publications in the field of educational data mining is growing yearly [109,110], the research efforts focused on developing models that can estimate learning outcomes are still unsatisfying.”
- Large Language Models for Education: A survey and outlook (2024, positive): “Furthermore, advancements in AI techniques have introduced new analytical tools and insights that continue to enhance pedagogical practices in education.”