Paper
22 April 2022 Research on AI-assisted grading of math questions based on deep learning
Hongxue Yang, Kongtao Zhu
Author Affiliations +
Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740X (2022) https://doi.org/10.1117/12.2628649
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China
Abstract
AI-assisted grading system saves great amount of time for teachers. Current application focus on grading techniques for Chinese or English composition, where high frequency features contains major feature for the output. In contrast, grading math problems requires sophisticated precision and rigorous logic. This paper illustrated an AI-assisted grading system for math questions. This system learns the mapping from students’ free response in math exams to output score, requiring little effort from teachers. The system extracts equations and reasoning and compares them to output a final score. We proposed a new network, CycleLatex, as an efficient solution to image-to-latex translation problems. It is capable of unsupervised learning and has shown state-of-art performance on handwriting and multi-line equations. The system also implements a parser which is capable of comparing most equations and reasoning at a reasonable speed.
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Hongxue Yang and Kongtao Zhu "Research on AI-assisted grading of math questions based on deep learning", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740X (22 April 2022); https://doi.org/10.1117/12.2628649
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KEYWORDS
Data modeling

Latex

Mathematics

Neural networks

Statistical modeling

Detection and tracking algorithms

Astatine

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