| dc.description.abstract | In recent years, educational resources have gradually digitized. More and more teachers put materials and assignments on internet to enable students to learn classroom knowledge without being restricted by time and space. Automatic grading is the issue that is born in response to the trend of digital teaching. Differ from manual grading, automatic grading is faster and able to detect the problem that require a lot of time and effort to complete, such as textbook concept extraction or plagiarism detection. As long as a good grading standard is set, the machine automatic grading can be used as a good scoring basis.
In view of this, this study implements an automatic grading system through corpus-based text mining method. Among them, unlike the existing semantic-based analysis and grading method, we propose a concept-based grading method to extract the key concepts in the course materials to grade student assignments. In this study, four different automatic scoring methods were implemented, namely the Latent semantic analysis (LSA) and Explicit semantic analysis (ESA) in semantic-based and the TF-IDF method and TextRank method in concept-based.
In addition to the implementation of the scoring system, we also do plagiarism detection by comparing the texts of student assignments and course materials. The automatic scoring framework is completed in the following steps: the student′s homework text is pre-processed, and the text is compared with the course materials. Finally, the machine′s score and manual score are compared, and K-means and Spearman′s correlation are used to verify the accuracy of the score. This paper focuses on the grading effects of four automatic grading methods under different curriculum design and reports text category. In our experimental course, the TextRank grading method in the concept-based got the best result.
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