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Download full textBosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Direct linkLi, Yanyan; Huang, Zhinan; Jiang, Menglu; Chang, Ting-Wen – Educational Technology & Society, 2016
Incorporating scientific fundamentals via engineering through a design-based methodology has proven to be highly effective for STEM education. Engineering design can be instantiated for learning as they involve mental and physical stimulation and develop practical skills especially in solving problems. Lego bricks, as a set of toys based on design…
Descriptors: Foreign Countries, Elementary School Students, Grade 4, Elementary School Science
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
