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
(past) 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 451 | PDF Downloads 320 | DOI https://doi.org/10.5281/zenodo.5275310
Page 1-15
Modeling NAEP Test-Taking Behavior Using Educational Process Analysis
Abstract 585 | PDF Downloads 312 | DOI https://doi.org/10.5281/zenodo.5275312
Page 16-54
AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability
Abstract 478 | PDF Downloads 648 | 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 322 | PDF Downloads 239 | 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 286 | PDF Downloads 141 | DOI https://doi.org/10.5281/zenodo.5275308
Page i