%0 Journal Article %1 Slater20092016 %A Slater, Stefan %A Joksimović, Srećko %A Kovanovic, Vitomir %A Baker, Ryan S. %A Gasevic, Dragan %D 2016 %J Journal of Educational and Behavioral Statistics %K bigdata educationaldatamining learninganalytics review textmining visualization %R 10.3102/1076998616666808 %T Tools for Educational Data Mining: A Review %U http://jeb.sagepub.com/content/early/2016/09/19/1076998616666808.abstract %X In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.

@article{Slater20092016, abstract = {In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.}, added-at = {2016-09-27T08:36:54.000+0200}, author = {Slater, Stefan and Joksimović, Srećko and Kovanovic, Vitomir and Baker, Ryan S. and Gasevic, Dragan}, biburl = {https://www.bibsonomy.org/bibtex/2fbcc1525837b66bace42b82e1d8757b0/ereidt}, doi = {10.3102/1076998616666808}, eprint = {http://jeb.sagepub.com/content/early/2016/09/19/1076998616666808.full.pdf+html}, interhash = {dba7ed05b5bde748a804f69e0b611f34}, intrahash = {fbcc1525837b66bace42b82e1d8757b0}, journal = {Journal of Educational and Behavioral Statistics}, keywords = {bigdata educationaldatamining learninganalytics review textmining visualization}, timestamp = {2020-01-19T10:25:33.000+0100}, title = {Tools for Educational Data Mining: A Review}, url = {http://jeb.sagepub.com/content/early/2016/09/19/1076998616666808.abstract}, year = 2016 }