Vol 13 No 2 (2021): Scientific Findings from the NAEP Data Mining Competition

View 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 814 | PDF Downloads 656
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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 1480 | PDF Downloads 711
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Page 16-54

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

Nigel Bosch
Abstract 1222 | PDF Downloads 1263
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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 763 | PDF Downloads 577
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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 676 | PDF Downloads 415
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