A corpus-based lexical and grammatical error identification: L2 learners academic writing

Writing in English has never been an easy task to many second language (L2) learners. Many of them perform poorly in their English academic writing where numerous lexical and grammatical errors are found in their report. Therefore, this thesis investigates the difficulties faced by UTHM learners inv...

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Main Author:	Talib, Salleh
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/1170/1/24p%20SALLEH%20BIN%20TALIB.pdf http://eprints.uthm.edu.my/1170/2/SALLEH%20BIN%20TALIB%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1170/3/SALLEH%20BIN%20TALIB%20WATERMARK.pdf
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id	my-uthm-ep.1170
record_format	uketd_dc
spelling	my-uthm-ep.11702021-08-22T08:41:52Z A corpus-based lexical and grammatical error identification: L2 learners academic writing 2021-04 Talib, Salleh T Technology (General) Writing in English has never been an easy task to many second language (L2) learners. Many of them perform poorly in their English academic writing where numerous lexical and grammatical errors are found in their report. Therefore, this thesis investigates the difficulties faced by UTHM learners involved in academic writing by identifying and analyzing errors made by them with the application of error analysis procedures. This research attempts to find out the types and patterns of errors in which it focuses on the frequency of the lexical and grammatical errors of the L2 learners in their writing. Errors were investigated and identified based on students’ 36 progress and final reports which were assembled from first year engineering students; named as the Learner Corpus Universiti Tun Hussein Onn Malaysia(LCUTHM). The LCUTHM was analyzed by means of linguistics Natural Language Processing tools (NLP) such as CLAWS 5 tag set, Markin Version 4 and categorized by MonoConc Pro II in the form of word lists. Data were also analyzed using Statistical Package for the Social Science (SPSS) software to determine the major errors learners committed in learners’ written work. The findings reveal that the major lexical and grammatical error categories made by learners were “Missing Word”, “Repetition”, and “Verb Form”. Finally, the integration of technology and the linguistics Natural Language Processing (NLP) tools can provide a fast and more effective method in assisting teachers in identifying errors, and designing syllabus in improving the language skills and achievement of L2 learners in their academic writing. 2021-04 Thesis http://eprints.uthm.edu.my/1170/ http://eprints.uthm.edu.my/1170/1/24p%20SALLEH%20BIN%20TALIB.pdf text en public http://eprints.uthm.edu.my/1170/2/SALLEH%20BIN%20TALIB%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1170/3/SALLEH%20BIN%20TALIB%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Faculty of Applied Science and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Talib, Salleh A corpus-based lexical and grammatical error identification: L2 learners academic writing
description	Writing in English has never been an easy task to many second language (L2) learners. Many of them perform poorly in their English academic writing where numerous lexical and grammatical errors are found in their report. Therefore, this thesis investigates the difficulties faced by UTHM learners involved in academic writing by identifying and analyzing errors made by them with the application of error analysis procedures. This research attempts to find out the types and patterns of errors in which it focuses on the frequency of the lexical and grammatical errors of the L2 learners in their writing. Errors were investigated and identified based on students’ 36 progress and final reports which were assembled from first year engineering students; named as the Learner Corpus Universiti Tun Hussein Onn Malaysia(LCUTHM). The LCUTHM was analyzed by means of linguistics Natural Language Processing tools (NLP) such as CLAWS 5 tag set, Markin Version 4 and categorized by MonoConc Pro II in the form of word lists. Data were also analyzed using Statistical Package for the Social Science (SPSS) software to determine the major errors learners committed in learners’ written work. The findings reveal that the major lexical and grammatical error categories made by learners were “Missing Word”, “Repetition”, and “Verb Form”. Finally, the integration of technology and the linguistics Natural Language Processing (NLP) tools can provide a fast and more effective method in assisting teachers in identifying errors, and designing syllabus in improving the language skills and achievement of L2 learners in their academic writing.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Talib, Salleh
author_facet	Talib, Salleh
author_sort	Talib, Salleh
title	A corpus-based lexical and grammatical error identification: L2 learners academic writing
title_short	A corpus-based lexical and grammatical error identification: L2 learners academic writing
title_full	A corpus-based lexical and grammatical error identification: L2 learners academic writing
title_fullStr	A corpus-based lexical and grammatical error identification: L2 learners academic writing
title_full_unstemmed	A corpus-based lexical and grammatical error identification: L2 learners academic writing
title_sort	corpus-based lexical and grammatical error identification: l2 learners academic writing
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Applied Science and Technology
publishDate	2021
url	http://eprints.uthm.edu.my/1170/1/24p%20SALLEH%20BIN%20TALIB.pdf http://eprints.uthm.edu.my/1170/2/SALLEH%20BIN%20TALIB%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1170/3/SALLEH%20BIN%20TALIB%20WATERMARK.pdf
_version_	1747830749361340416

A corpus-based lexical and grammatical error identification: L2 learners academic writing

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