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

Nathan A. Levin
Abstract 717 | PDF Downloads 532 | DOI https://doi.org/10.5281/zenodo.5275310
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Page 1-15

Modeling NAEP Test-Taking Behavior Using Educational Process Analysis

Nirmal Patel, Aditya Sharma, Tirth Shah, Derek Lomas
Abstract 1365 | PDF Downloads 597 | DOI https://doi.org/10.5281/zenodo.5275312
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Page 16-54

AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability

Nigel Bosch
Abstract 1058 | PDF Downloads 1112 | DOI https://doi.org/10.5281/zenodo.5275314
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Page 55-79

Applying Psychometric Modeling to aid Feature Engineering in Predictive Log-Data Analytics: The NAEP EDM Competition

Fabian Zehner, Beate Eichmann, Tobias Deribo, Scott Harrison, Daniel Bengs, Nico Andersen, Carolin Hahnel
Abstract 635 | PDF Downloads 482 | DOI https://doi.org/10.5281/zenodo.5275316
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Page 80-107

Editorial Acknowledgments and Introduction to the Special Issue for the NAEP Data Mining Competition

Ryan S. Baker, Neil T. Heffernan, Thanaporn Patikorn Patikorn, Carol M. Forsyth, Irvin R. Katz
Abstract 569 | PDF Downloads 328 | DOI https://doi.org/10.5281/zenodo.5275308
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