Strategies for Teaching English in a Computer-Assisted Writing Learning Model

Abstract

Abstract The computer-aided writing system first builds an English writing corpus, which mainly includes several aspects such as corpus selection, corpus preprocessing, formula algorithm design, sentence breaking, and judging whether the sentences are aligned or not. Anchor alignment cannot perform a job alone; it must work in conjunction with named entity recognition technology to study the connection and role between the two. Jieba participle utilizes the idea of the HMM model, which is researched and calculated by the Viterbi algorithm, to recognize new words outside the dictionary. A computer-assisted writing system is introduced into English language teaching to promote conscious and deep student participation, activate classroom teaching, and improve learning effectiveness. The results showed that there was also a significant correlation between text length and English writing ability (P=0.003) and on T-units and English writing ability (P=0.001). Statistical significance was reached for all three indicators of text length (P=0.001), T-units (P=0.001), and English writing proficiency (P=0.002) between groups of high and low subgroups. The vocabulary recommendation function usage record revealed that each student used it 6.21 times on average and had a liking for it 4.32 times on average. 20 students agreed that the system was helpful for their writing. The two groups of students’ writing scores differed significantly (0.001) from their respective pre-test scores. The computerized writing instruction system was more helpful for the student’s performance.