Research on AI-assisted grading of math questions based on deep learning

Hongxue Yang; Kongtao Zhu

doi:10.1117/12.2628649

22 April 2022 Research on AI-assisted grading of math questions based on deep learning

Hongxue Yang, Kongtao Zhu

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.

Citation Download Citation

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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