Study on Automatic Classification of English Tense Exercises for Intelligent Online Teaching
With online teaching becoming one of the normalized teaching methods,people put forward higher quality teaching de-mands.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 au-tomatic classification method for online education resources based on natural language processing technology,and conduct experi-ments on the automated classification of eight English tense exercises,which are the key contents of middle school English gram-mar teaching.The experiment collects more than 90 000 English tense exercises both online and offline.After data cleaning,ap-proximately 30 000 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 ten-ses,the experimental result can meet the practical needs of automatic classification and organization of English education re-sources,intelligent correction and personalized push of exercises,intelligent Q&A.It provides a feasible idea and solution for im-proving the quality of online teaching and integrating online education resources.
Online teachingNatural Language ProcessingEnglish tense classification
NETL
NSTL
万方数据
