A Computer-Assisted Applied Corpus Analysis of Writing Errors Made by Senior Students of The English Language Department Faculty of Arts at Tanta University

Abstract

The dissertation investigates writing errors among Senior Students in the English Language Department using computer-assisted corpus analysis. It aims to explore various types of errors in Lexical, Syntactic, Semantic, and Mechanical aspects. A mixed approach was used, using virtualwritingtutor.com to identify different categories of errors. The study aims to uncover prevalent errors in English writing and compare and evaluate errors based on gender (male and female). The Ant Concordance Tool software was used to identify common sources and causes of errors. A quantitative analysis was conducted using SPSS with a sample size of 494 participants. Results showed that male students exhibited significantly fewer errors in syntactic, lexical, and semantic aspects, but not in mechanical aspects. The study highlights the importance of understanding and addressing writing errors among senior students in the English Language Department.