Educational data mining
Draft 1
Editor: Michel C. Desmarais, Polytechnique Montreal
Contributor: Ryan S.J.d. Baker, Worcester Polytechnic Institute
Contents
Definition
Educational Data Mining is a term used for processes designed for the analysis of data from educational settings to better understand students and the settings which they learn in.
Comments on the history
The term emerged from a series of workshops organized around the theme of analysing student log data, the first of which can be traced back to the ITS 2000 Conference in Montreal. In 2005, the first workshop bearing the name “Educational Data Mining” (EDM) was held in Pittsburgh along with the AAAI Conference (Association for the Advancement of Artificial Intelligence). EDM is now the name of an international conference held annually since 2008. In addition, a related conference on Learning analytics (LAK2011) appeared in 2011. The first issue of the International Journal of EDM, an on-line and open-access publication hosted at www.educationaldatamining.org/JEDM, was published in 2009. In 2011, the International Educational Data Mining Society was founded to organize the conference and journal.
Related terms
Learning analytics, usage analysis, learner data, student log data, knowledge discovery, data mining, statistical learning, data mining, psychometrics, student modeling, classification, regression, clustering, factor analysis, association rule mining, visualization, discovery with models, database.
Translation issues
French: "analytique des données éducationnelles"
Disciplinary issues
The field of Educational Data Mining (EDM) draws methods and theory from a number of disciplines, such as data mining, knowledge discovery, psychometrics, and statistical learning. It aims to contribute models and findings that can help design and implement innovative learning applications and environments, as well as contributing to theory in educational psychology and other areas of education. EDM methods include (but are not limited to) classification, regression, factor analysis, clustering, relationship mining, knowledge prediction, correlation mining, association rule mining, visualization, domain structure discovery, discovery with models.
Key references
[1] Baker R., Yacef K. (2009) The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining. 1, 3-17.
[2] Baker R.S.J.d. (2010) Data Mining For Education. In: Peterson P., Baker R., Mcgaw B. (eds.) International Encyclopedia of Education (3rd edition) (7, 112-118). Oxford, UK: Elsevier.
[3] Romero C., Ventura, S. (2007) Educational Data Mining: A Survey from 1995 to 2005. Expert Systems with Applications. 33, 125-146.
[4] Romero C., Ventura S. (2010) Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 6, 601 - 618.
[5] Koedinger K.R., Cunningham K. A. S., Leber B. (2008) An open repository and analysis tools for fine-grained, longitudinal learner data. In: Proceedings of the 1st International Conference on Educational Data Mining, 157-166.
[6] EDM, International Working Group on Educational Data Mining: Educational data mining. (2009)
