English Education Students' Thesis Abstracts Error Analysis

Abstract

Over the last decades, applied linguistics and language teaching/learning have investigated language errors committed by learners for both diagnostic and prognostic purposes. Initially, error analysis was conducted manually and involved a limited number of corpus. However, computer software advancement has facilitated much larger amounts of data analysis. This study aimed at analyzing the errors identified in undergraduate thesis abstracts written by 28 students of an English Education study program in Jakarta. Data were analyzed using UAM Corpus Tool. The results show that, successively, the types of errors most frequently committed in the corpus are: (1) grammatical errors; (2) phrasing errors; and (3) punctuation errors; while the rarest errors are pragmatic errors and lexical errors. These findings indicate that the students need to improve their mastery of grammatical rules, ability to prevent their first language interference, and skills to use correct punctuation to empower them to write more effective thesis abstracts.