Analysis Of Tense Errors In Chinese College Non-English Major Students' Writing

With the theoretical support of Error Analysis and the Bo Bing's sixteen tense system,this research is devoted to identify, classify and analyze the tense errors among three datagroups, two sub-corpora of CLEC (Chinese Learner English Corpus) and150compositionssampled from2010-2011term papers of Grade two students in Shanxi Normal University soas to get a longitudinal view of the tense application conditions between Chinese collegenon-English major sophomores and seniors, as well as a cross-sectional view of tenseapplication condition for non-English major sophomores of our university in comparison withthat of the overall Chinese college non-English major students.Tense errors of the three data groups are initially extracted by the text-retrieval software,AntConc, then, with the assistance of my classmates, a more specific manual work on theidentification and classification of tense errors are promoted. The analysis in the present studymainly concerns with the distribution of error occurrences among all the sixteen tenses as wellas the distribution of four types of tense errors across the three data groups. The distributingcharacteristics of tense errors and tense error types across ST3and ST4reflect that thesituation of tense command is not closely corresponding to the extended time of English studyand the enhancement of the general proficiency of English. And the characteristics of thedistribution for tense errors and tense error types across ST3and the150samples indicate abetter command of tense selection but a relatively weak mastery over the technique of tenseagreement for the non-English sophomores in our university.More detailed exemplification and illustration for the most problematic tense for eachof the four types of tense errors across the present database are also provided. Based on theresult acquired by the analysis mentioned above, the simple present, simple past and presentperfect tenses turn out to be the most problematic tenses with more error occurrences amongall the sixteen tenses across the three data groups. And the simple past tense manifests with the most tense choice errors, the simple present tense with the most subject-verb agreementerrors, present perfect tense with the most tense related verb form errors and tensedisagreement errors within a sentence or among adjacent sentences most frequently occurunder the circumstance that simple past is replaced by simple present.