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This page provides an overview of 1 article related to 'educational data mining'. Note that filters may be applied to display a sub-set of articles in this category (see FAQs on filtering for usage tips). Select this link to remove all filters.

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Educational Data Mining (EDM) describes a research field concerned with the application of data mining to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. A key area of EDM is mining computer logs of student performance. Another key area is mining enrollment data. Key uses of EDM include predicting student performance, and studying learning in order to recommend improvements to current educational practice. EDM can be considered one of the learning sciences, as well as an area of data mining. A related field is learning analytics. (Excerpt from Wikipedia article: Educational Data Mining)

Key statistics

Metadata related to 'educational data mining' (as derived from all content tagged with this term):

  • Number of articles referring to 'educational data mining': 2 (0.1% of published articles)
  • Total references to 'educational data mining' across all Ariadne articles: 13
  • Average number of references to 'educational data mining' per Ariadne article: 6.50
  • Earliest Ariadne article referring to 'educational data mining': 2013-07
  • Trending factor of 'educational data mining': 0 (see FAQs on monitoring of trends)

See our 'educational data mining' overview for more data and comparisons with other tags. For visualisations of metadata related to timelines, bands of recency, top authors, and and overall distribution of authors using this term, see our 'educational data mining' usage charts. Usage chart icon

Top authors

Ariadne contributors most frequently referring to 'educational data mining':

  1. manolis mavrikis (see articles on this topic by this author)
  2. patricia charlton (see articles on this topic by this author)
  3. demetra katsifli (see articles on this topic by this author)

Note: Links to all articles by authors listed above set filters to display articles by each author in the overview below. Select this link to remove all filters.

Title Article summary Date

The Potential of Learning Analytics and Big Data

Patricia Charlton, Manolis Mavrikis and Demetra Katsifli discuss how the emerging trend of learning analytics and big data can support and empower learning and teaching.

July 2013, issue71, feature article

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by Dr. Radut