Comment Text Grading for Chinese Graduate Academic Dissertation Using Attention Convolutional Neural Networks

Yupei Zhang, Yaya Zhou, Min Xiao, Xuequn Shang

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Educational big data connects learning science with data science, where various educational problems are formulating into data mining tasks towards new solutions and new discoveries. This paper provides a path of automatically grading graduate academic dissertations according to the expert-given comment texts. The proposed method fed comment texts to an attention convolutional neural network consisted of an embedding layer, an attention mechanism layer, a convolutional layer, and a fully connected neural network, where the data imbalance issue was handled by data augmentations. The used comment texts were collected from 943 students spreading at 145 universities in China, where these review comments were yielded by experts to grade the academic dissertations. The results from the proposed method achieve a classification accuracy of 77% that gains 12% and 15% implementations compared to the classical convolutional neural network and the linear support vector machine. However, the result analyses show that there are many conflicts between expert-given comments and their given grades in the collected data. This study provides an automatic tool that could remove these conflicts in the dissertation review, leading to more objective dissertation grades.

源语言英语
主期刊名ICSAI 2021 - 7th International Conference on Systems and Informatics
编辑Jianxi Yang, Kenli Li, Wanqing Tu, Zheng Xiao, Libo Wang
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781665426244
DOI
出版状态已出版 - 2021
活动7th International Conference on Systems and Informatics, ICSAI 2021 - Chongqing, 中国
期限: 13 11月 202115 11月 2021

出版系列

姓名ICSAI 2021 - 7th International Conference on Systems and Informatics

会议

会议7th International Conference on Systems and Informatics, ICSAI 2021
国家/地区中国
Chongqing
时期13/11/2115/11/21

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