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.