Using Deep Learning to Choose Optimal Smoothing Values for Equating.

Abstract

Test developers typically use alternate test forms to protect the integrity of test scores. Because test forms may differ in difficulty, scores on different test forms are adjusted through a psychometrical procedure called equating. When conducting equating, psychometricians often apply smoothing methods to reduce random error of equating resulting from sampling. During the process, they compare plots of different smoothing degrees and choose the optimal value when using the cubic spline postsmoothing method. This manual process, however, could be automated with the help of deep learning-a machine learning technique commonly used for image classification. In this study, a convolutional neural network was trained using human-classified postsmoothing plots. The trained network was used to choose optimal smoothing values with empirical testing data, which were compared to human choices. The agreement rate between humans and the trained network was as large as 71%, suggesting the potential use of deep learning for choosing optimal smoothing values for equating.