A framework for effectively utilising human grading input in automated short answer grading

Lui, Andrew Kwok-Fai, Ng, Sin-Chun and Cheung, Stella Wing-Nga (2022) A framework for effectively utilising human grading input in automated short answer grading. International Journal of Mobile Learning and Organisation, 16 (3). pp. 266-286. ISSN 1746-7268

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Official URL: https://dx.doi.org/10.1504/IJMLO.2022.10048071

Abstract

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.

Item Type: Journal Article
Keywords: automated short answer grading, semi-supervised clustering, MOOCs, automated grading
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 09 Jun 2022 10:42
Last Modified: 26 Aug 2022 10:31
URI: https://arro.anglia.ac.uk/id/eprint/707667

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