Educational data mining: A survey and a data mining-based analysis of recent works

• The work extends prior educational data mining (EDM) reviews and updates its history.
• A data mining (DM) profile is set to depict the DM ground that supports EDM works.
• An EDM approach profile is set to provide key traits for depicting EDM approaches.
• A trait-value pattern is set to depict most of descriptive and predictive EDM models.
• Three disciplines, tasks, methods, algorithms are the most used for building EDM works.

This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.