NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 2 results Save | Export
Using Process Data to Understand Problem-Solving Strategies and Processes for Drag-and-Drop Items in a Large-Scale Mathematics Assessment
Peer reviewed Peer reviewed
Direct linkDirect link
Jiang, Yang; Gong, Tao; Saldivia, Luis E.; Cayton-Hodges, Gabrielle; Agard, Christopher – Large-scale Assessments in Education, 2021
In 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to…
Descriptors: Data Use, Learning Analytics, Test Items, Measurement
AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Bosch, 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