Affiliation:
1. College of Education, Purdue University, West Lafayette, Indiana 47907, USA;
2. College of Education, University of Washington, Seattle, Washington 98195, USA;
3. Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820, USA;
Abstract
Educational measurement assigns numbers to individuals based on observed data to represent individuals’ educational properties such as abilities, aptitudes, achievements, progress, and performance. The current review introduces a selection of statistical applications to educational measurement, ranging from classical statistical theory (e.g., Pearson correlation and the Mantel–Haenszel test) to more sophisticated models (e.g., latent variable, survival, and mixture modeling) and statistical and machine learning (e.g., high-dimensional modeling, deep and reinforcement learning). Three main subjects are discussed: evaluations for test validity, computer-based assessments, and psychometrics informing learning. Specific topics include item bias detection, high-dimensional latent variable modeling, computerized adaptive testing, response time and log data analysis, cognitive diagnostic models, and individualized learning.
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Cited by
8 articles.
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