posted on 2023-08-30, 20:00authored byAndrew Kwok-Fai Lui, Sin-Chun Ng, Stella Wing-Nga Cheung
Short answer questions are effective for recall knowledge assessment. Grading a large amount of short answers is costly and time consuming. To apply short answer questions on MOOCs platforms, the issues of scalability and responsiveness must be addressed. Automated grading uses a computing process and a machine learning grading model to classify answers into correct, wrong, and other levels of correctness. The divide-and-grade approach is proven effective in reducing the annotation effort needed for the learning the grading model. This paper presents an improvement on the divide-and-grade approach that is designed to increase the utility of human actions. A novel short answer grading framework is proposed that addresses the selection of impactful answers for grading, the injection of the ground-truth grades for steering towards purer final clusters, and the final grade assignments. Experiment results indicate the grading quality can be improved with the same level of human actions.
History
Refereed
Yes
Volume
16
Issue number
3
Page range
266-286
Publication title
International Journal of Mobile Learning and Organisation