现代信息科技2025，Vol.9Issue(12)：79-84,6.DOI:10.19850/j.cnki.2096-4706.2025.12.016
大语言模型驱动的多模态实验报告自动批改
Automatic Correction of Multimodal Experimental Reports Driven by LLM
摘要
Abstract
Automatic correction of experimental reports is an important task in the field of intelligent education.Following the OBE concept,the paper reproduces the teacher's correction idea,transforms the scoring items into questions,and coordinates the multimodal response information such as text,tables,and pictures to score,so as to be close to the actual teaching and curriculum construction needs.In the understanding and scoring stage of multimodal information,on the basis of Deep Learning,LLM is introduced to realize the content extraction and transformation of table questions,and solve the difficulties of positioning and logical discrimination.For text content,BERT is used to understand.For the image content,the self-training model constructed by the combination of BERT and ResNet-18 is used to scale the image matching weights for the image feature evaluation in graphic questions.The scheme uses small sample data for training,adapts to different subject experiments,and overcomes the pain points such as insufficient generalization and migration caused by relying on a large amount of data training.Through the correction test of two courses,the average accuracy of the report score reaches 92.20%,bridging the gap of automatic correction of non-customized experimental reports.关键词
实验报告自动批改/深度学习/大语言模型Key words
automatic correction of experimental report/Deep Learning/LLM分类
信息技术与安全科学引用本文复制引用
徐继宁,黄楠,宋浩..大语言模型驱动的多模态实验报告自动批改[J].现代信息科技,2025,9(12):79-84,6.基金项目
北京市教委北京市数字教育研究重点课题-面向卓越人才培养的多维数字化学习空间构建支持(BDEC2022619001) （BDEC2022619001）
