Use neural networks to recognize students' handwritten letters and incorrect symbols
This addresses the repetitive task of grading for educators, but it appears incremental as it applies a standard neural network approach to a specific educational data scenario.
The paper tackles the problem of automatically correcting students' handwritten multiple-choice answers by treating it as an image multi-classification task, setting up five classifications (four for correct options and one for incorrect symbols) to handle non-standard writing, but no concrete results or numbers are reported.
Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options.