面向智能在线教学的英语时态习题自动分类研究  

Study on Automatic Classification of English Tense Exercises for Intelligent Online Teaching

作　　者：屠鑫 张伟[2] 李继东 李美姣 龙相波 TU Xin;ZHANG Wei;LI Jidong;LI Meijiao;LONG Xiangbo(College of Vocational and Continuing Education,Yunnan University,Kunming 650091,China;Key Laboratory of Intelligent Information Processing and Control,Chongqing Three Gorges University,Chongqing 404100,China)

机构地区：[1]云南大学职业与继续教育学院,昆明650091 [2]重庆三峡学院智能信息处理与控制重庆市高校重点实验室,重庆404100

出　　处：《计算机科学》2024年第4期353-358,共6页Computer Science

基　　金：2022年云南大学专业学位研究生实践创新基金项目(ZC-22222893)。

摘　　要：随着在线教学逐渐成为常态化的教学方式之一,人们对其提出了更高质量的教学需求。各种在线教学平台及互联网上海量的教育资源大大便利了众多学习者,但同时也存在着教育资源丰富但质量参差不齐、缺乏有效的分类整合以及主要依靠人工整理等问题,这就导致人们在获取在线教育资源时往往需要花费大量的时间和精力来进行检索、甄别和整理。针对在线教育资源现存的不足,文中提出了基于自然语言处理技术的在线教育资源自动分类方法,并以中学英语语法重点内容八大英语时态的习题自动分类为例,收集了线上及线下共9万余条时态类习题,通过数据清洗,最终选择3万余条语句构建数据集,并构建BERT微调文本分类模型,通过训练模型实现了对八大时态的自动分类,整体分类准确率达到86.15%,其中对一般现在时的识别准确率最高,达到93.88%。可以一定程度上满足中学英语时态类教育资源自动分类整理、习题智能批改及个性化推送、智能问答等现实需要,为提高在线教学质量,整合在线教育资源提供可行的思路和解决方案。With online teaching becoming one of the normalized teaching methods,people put forward higher quality teaching demands.Various online teaching platforms and the amount of educational resources on the Internet have greatly facilitated many learners.However,there are also some problems in educational resources such as uneven quality,lack of effective classification and integration,and mainly rely on manual sorting,which lead to people spending too much time and energy to search,screen and sort online educational resources.Considering the existing shortcomings of online education resources,this paper proposes an automatic classification method for online education resources based on natural language processing technology,and conduct experiments on the automated classification of eight English tense exercises,which are the key contents of middle school English grammar teaching.The experiment collects more than 90000 English tense exercises both online and offline.After data cleaning,approximately 30000 sentences are selected to construct a dataset,and a BERT fine-tuning text classification model is constructed.By training the model,automatic classification of the eight tenses is realized with an overall classification accuracy of 86.15%.And the recognition accuracy for the present tense is the highest,reaching 93.88%.To a certain extent,in terms of English tenses,the experimental result can meet the practical needs of automatic classification and organization of English education resources,intelligent correction and personalized push of exercises,intelligent Q&A.It provides a feasible idea and solution for improving the quality of online teaching and integrating online education resources.

关 键 词：在线教学 自然语言处理 英语时态分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]