The State of Educational Data Mining in 2009: A Review and Future Visions

Состояние интеллектуального анализа образовательных данных в 2009 году: обзор и перспективы развития
Ryan S. Baker, Kalina Yacef
2009-10-01

Educational Data Miningdiscovery with modelspredictionrelationship miningresearch trends
We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.
1
It categorizes the diversity of EDM research, examines the problems addressed by different methods, and discusses influential papers published between 1995 and 2005.
2
Relationship mining became less frequent within the Educational Data Mining community in 2008 and 2009.
3
The review compares methodological profiles across EDM’s early years, 2008, and 2009, highlighting changes in research emphasis.
4
The review identifies a shift in Educational Data Mining toward predictive research during 2008 and 2009.
5
Using existing models to make scientific discoveries, termed “discovery with models,” emerged as an important EDM research direction.

Educational Data Mining (EDM) research and its research community

The historical development, methodological trends, research-problem categories, and influential publications of EDM, including shifts toward prediction and discovery with models and reduced relationship mining

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2009-10-01
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Ryan S. Baker
Kalina Yacef
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