Automated scoring of free drawings or images as responses has yet to be
utilized in large-scale assessments of student achievement. In this study, we
propose artificial neural networks to classify these types of graphical
responses from a computer based international mathematics and science
assessment. We are comparing classification accuracy of convolutional and
feedforward approaches. Our results show that convolutional neural networks
(CNNs) outperform feedforward neural networks in both loss and accuracy. The
CNN models classified up to 97.71% of the image responses into the appropriate
scoring category, which is comparable to, if not more accurate, than typical
human raters. These findings were further strengthened by the observation that
the most accurate CNN models correctly classified some image responses that had
been incorrectly scored by the human raters. As an additional innovation, we
outline a method to select human rated responses for the training sample based
on an application of the expected response function derived from item response
theory. This paper argues that CNN-based automated scoring of image responses
is a highly accurate procedure that could potentially replace the workload and
cost of second human raters for large scale assessments, while improving the
validity and comparability of scoring complex constructed-response items.

本研究探讨基于卷积神经网络和前馈神经网络的自动化打分方法在国际数学和科学考试中将学生的绘画或图像回答分类的准确性。研究发现，相对于前馈神经网络，卷积神经网络能够更加精确地分类图像响应，并且可以取代人工评分，提高成绩评分的有效性和可比性。