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Download full textBosch, 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
Vanneman, Alan – Focus on NAEP, 1997
The National Assessment of Educational Progress (NAEP) has been collecting data on student achievement since 1969. It currently maintains three different assessments: long-term trends, cross-sectional national, and cross-sectional state-by-state data. Although the data are available to researchers outside the Federal Government, limited use has…
Descriptors: Academic Achievement, Computer Software, Computer Software Evaluation, Computer Uses in Education
Johnson, Eugene G.; And Others – 1987
This document is the users' guide for Version 1.0 of the Public-Use data tapes compiled by the National Assessment of Educational Progress (NAEP), 1985-86. The Public-Use tapes are produced to allow outside researchers access to the NAEP data. The tapes accompanying this guide, one for grade 3/age 9, one for grade 7/age 13, and one for grade…
Descriptors: Academic Achievement, Computer Software, Computer Storage Devices, Data Analysis
