Educational Data Mining: An Advance for Intelligent Systems in Education

descriptionPublicationkeyboard_double_arrow_right Article 01 May 2014Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Intelligent Systems, volume 29, pages 78-82 (issn: 1541-1672, eissn: 1941-1294,

Copyright policy )

Authors: Ryan S. Baker 0001;

doi: 10.1109/mis.2014.42

Educational Data Mining: An Advance for Intelligent Systems in Education

- Summary
- Metrics

Abstract

Educational data mining methods have been successful at modeling a range of phenomena relevant to student learning in online intelligent systems. Here, the author considers the current state of the field, outlining recent strides and remaining challenges.

Related Organizations

King’s University
United States
Columbia University
United States

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	88
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%