Vol. 13 No. 2 (2021): Scientific Findings from the NAEP Data Mining Competition
Special issue on the NAEP Data Mining Competition.
Ryan S. Baker, Neil T. Heffernan, Thanaporn Patikorn, Carol M. Forsyth, and Irvin R. Katz, Editors
Published: 2021-08-26
Scientific Findings from the NAEP 2019 Data Mining Competition
Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments
Abstract 523 | PDF Downloads 379 | DOI https://doi.org/10.5281/zenodo.5275310
Page 1-15
Modeling NAEP Test-Taking Behavior Using Educational Process Analysis
Abstract 782 | PDF Downloads 397 | DOI https://doi.org/10.5281/zenodo.5275312
Page 16-54
AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability
Abstract 627 | PDF Downloads 787 | DOI https://doi.org/10.5281/zenodo.5275314
Page 55-79
Applying Psychometric Modeling to aid Feature Engineering in Predictive Log-Data Analytics: The NAEP EDM Competition
Abstract 385 | PDF Downloads 304 | DOI https://doi.org/10.5281/zenodo.5275316
Page 80-107
Editorial Acknowledgments and Introduction to the Special Issue for the NAEP Data Mining Competition
Abstract 334 | PDF Downloads 183 | DOI https://doi.org/10.5281/zenodo.5275308
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