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This paper aims to highlight a research on integrating technology into teaching and learning of second/foreign language writing.

In total, 18 empirical studies, involving a total of 1,281 second and foreign language learners, have been reviewed. These studies are selected from the following two databases: Web of Science and Google Scholar. The meta-analysis investigates how effect sizes vary depending on these moderators as follows: stage of writing, language context, learners’ educational level and language proficiency level.

The findings of this meta-analysis have indicated that technology has a large effect on second/foreign language writing (d = 1.7217). These findings have also revealed that the two stages of writing, drafting and editing, have received most of the researchers’ concern. In addition, high school and university learners have achieved a larger effect size of using technology in writing learning; beginner learners have achieved the smallest effect size.

To sum, the previous meta-analyses and reviews tried to explore the effect of computer on writing skills. However, some of them were limited to special groups (Williams and Beam, 2019) and some others analyzed very few studies (Little et al., 2018). Therefore, a comprehensive analysis of the effect of implementing technology in writing skills is needed. The purpose of this study is to perform a meta-analysis of the primary studies about the integration of technology into writing skills. The primary goals of this meta-analysis were to: examine the overall effects of implementing technology in writing; synthesize the relationship between technology and a number of moderators such as stages of writing, language context, learners’ target language proficiency and learners’ educational level (school and university).

In spite of the growing interest in using corpora in language teaching and learning, applying computers and software (especially corpora software) is still new in second language teaching and learning. In addition, employing a learner corpus-based perspective in teaching metadiscourse features in International English Language Testing System (IELTS) writing tasks is not reported to the best knowledge of the researchers. Understanding and spotting this gap, the purpose of this paper is to utilize a learner corpus-based approach in teaching metadiscourse features and investigate its possible impacts on IELTS writing performance of the Iranian second language learners. Therefore, this study addressed the following research questions and hypotheses.

The current research utilized a quasi-experimental research design. In addition, this research used a learner corpus-based methodology. The corpus-based methodology was exploited to enable the researchers to have access to a large body of authentic language materials. In other words, a corpus-based methodology was used due to the fact that it made it possible for the researchers to elicit the metadiscourse features from a large number of authentic writing materials and to employ them during the treatment process with authentic examples.

The findings showed that there was a positive correlation between teaching metadiscourse features and writing performance of IELTS learners; in that, teaching metadiscourse features could soar the writing performance of the subjects. In addition, interactional metadiscourse features had more impact than interactive metadiscourse features on writing performance.

The results of this research can have useful implications for second language teachers and learners as well as researchers in learner corpus as they can learn the creation and application of learner corpora in second language teaching and learning.

This paper is value in that it uses corpus software and methodology in teaching metadiscourse features in writing section of IELTS test.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

The development of written accuracy among learners of English as a Second Language (ESL) has always been a primary concern for ESL teachers and researchers in Applied Linguistics and Second Language Acquisition (SLA). While a vast body of research has examined written corrective feedback on students’ written products, few studies have focused on the development of written accuracy among Arabic speaking learners of English using automated feedback tools. This case study first examined the level of written accuracy of Bahraini learners of English in their second year at a higher education institute, highlighting the frequency of errors influenced by their first language (Arabic). The course following this first stage included a significant component of automated feedback on students’ writing; and this study explored the impact that the use of these feedback tools had on learners’ writing in English, tracking development over the course of an academic semester. A corpus of students’ initial writings and subsequent revisions was analysed to identify whether there was an improvement in the accuracy of students’ texts; and students’ perceptions were elicited.

ﻟط ﺎﻟ ﻣﺎ ﻛﺎ ن ﺗ طوﯾ ر اﻟدﻗﺔ ﻓ ﻲ ﻣﮭﺎ رة اﻟ ﻛﺗﺎﺑﺔ ﺑﯾ ن ﻣﺗ ﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾﺔ ﻛﻠ ﻐﺔ ﺛﺎﻧﯾﺔ اﻟ ﺷـ ﻐ ل اﻟ ﺷـﺎ ﻏل ﻟ ﻣﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹﻧﺟ ﻠﯾ زﯾﺔ واﻟﺑﺎﺣ ﺛﯾ ن ﻓ ﻲ اﻟﻠ ﻐو ﯾﺎ ت اﻟ ﺗ ط ﺑﯾﻘﯾ ﺔ و ا ﻛﺗ ﺳ ـﺎ ب اﻟﻠ ﻐﺔ اﻟ ﺛﺎﻧﯾ ﺔ. ﻓ ﻲ ﺣ ﯾ ن أ ن ﻣ ﺟ ﻣو ﻋ ﺔ ﻛﺑﯾ ر ة ﻣ ن ا ﻷ ﺑ ﺣ ﺎ ث ﻗد د ر ﺳ ـ ت ﻣ ﻼ ﺣ ظ ﺎ ت ﺗ ﺻ ـ ﺣ ﯾ ﺣ ﯾ ﺔ ﺧ ط ﯾ ﺔ ﻋ ﻠ ﻰ ﻛﺗﺎﺑﺎ ت اﻟط ﻼ ب ، ﻓﻘ د رﻛز ت د را ﺳ ــﺎ ت ﻗﻠﯾﻠ ﺔ ﻋ ﻠ ﻰ ﺗ ط وﯾ ر اﻟ دﻗﺔ ﻓ ﻲ ﻣﮭﺎ رة اﻟ ﻛﺗﺎﺑ ﺔ ﺑﯾ ن ﻣﺗ ﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹ ﻧ ﺟ ﻠﯾ زﯾ ﺔ اﻟﻧﺎ ط ﻘﯾ ن ﺑﺎﻟ ﻌرﺑﯾ ﺔ ﺑﺎ ﺳ ــﺗ ﺧ دا م أدو ا ت اﻟ ﺗ ﻐذﯾ ﺔ ا ﻻ ﺳ ـــﺗ ر ﺟ ﺎ ﻋ ﯾ ﺔ ﻋ ﺑ ر ا ﻹ ﻧﺗ ر ﻧ ت . ﺗ ﺗ ﻧ ﺎ و ل د ر ا ﺳ ـ ـ ﺔ ا ﻟ ﺣ ﺎ ﻟ ﺔ ھ ذ ه أ و ﻻً ﻣ ﺳ ـ ـ ﺗ و ى ا ﻟ د ﻗ ﺔ ا ﻟ ﻣ ﻛ ﺗ و ﺑ ﺔ ﻟ ﻠ ﻣ ﺗ ﻌ ﻠ ﻣ ﯾ ن ا ﻟ ﺑ ﺣ ر ﯾ ﻧ ﯾ ﯾ ن ﻟ ﻠ ﻐ ﺔ ا ﻹ ﻧ ﺟ ﻠ ﯾ ز ﯾ ﺔ ﻓ ﻲ ﺳـ ﻧﺗ ﮭم اﻟﺛﺎﻧﯾ ﺔ ﻓ ﻲ ﻣؤﺳـ ﺳـ ﺔ ﻟﻠﺗ ﻌﻠﯾم اﻟﻌﺎﻟ ﻲ ، ﻣﻊ اﻟﺗ رﻛﯾ ز ﻋﻠ ﻰ ﺗﻛرا ر أ ﺧطﺎ ء اﻟﺗدا ﺧل ﺑﯾ ن اﻟﻠ ﻐﺔ ا ﻻوﻟ ﻰ واﻟﺛﺎﻧﯾﺔ. ﺛم ﺗ ﺳـﺗﻛ ﺷـ ف اﻟد را ﺳـ ﺔ ﺗﺄﺛﯾ ر أ دو ا ت اﻟ ﻣ ﻼﺣظﺎ ت ﻋﺑ ر ا ﻹﻧﺗ رﻧ ت ﻋﻠ ﻰ ﻛﺗﺎﺑﺔ اﻟ ﻣﺗ ﻌﻠ ﻣﯾ ن ﺑﺎﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾﺔ ﻛﻠ ﻐﺔ ﺛﺎ ﻧﯾ ﺔ، وﺗ ﺗﺑ ﻊ اﻟﺗ طور ﺧﻼل اﻟﻔ ﺻ ـــ ل اﻟ د را ﺳـــ ﻲ. ﯾﺗ ﺿ ـــ ﻣ ن ا ﻟ ﺗ د ﺧ ل ا ﻟ ﻣ ط ﺑ ق ﻟ ﺗ ﺣ ﺳـ ﯾ ن ﺗ ﻧ ﻣ ﯾ ﺔ ﻣ ﮭ ﺎ ر ا ت ا ﻟ ﻣ ﺗ ﻌ ﻠ ﻣ ﯾ ن ﻣ ﻛ و ﻧًﺎ ﻣ ﮭ ﻣً ﺎ ﻟ ﻠ ﺗ ﻌ ﻠ ﯾ ق ﻋ ﺑ ر ا ﻹ ﻧ ﺗ ر ﻧ ت . ﻗ ﺎ ﻣ ت ا ﻟ د ر ا ﺳـ ﺔ ﺑ ﺗ ﺣ ﻠ ﯾ ل ﻣ ﺟ ﻣ و ﻋ ﺔ ﻣ ن ا ﻟ ﻛ ﺗ ﺎ ﺑ ﺎ ت ا ﻷ و ﻟ ﯾ ﺔ وا ﻟ ﻣرا ﺟﻌﺎ ت اﻟ ﻼﺣ ﻘﺔ ﻟﻠط ﻼ ب ، ﺑﺎﻹ ﺿ ﺎﻓﺔ إﻟ ﻰ ﻣر ا ﺟ ﻌﺎ ت اﻟ ﻧ ظ ر ا ء، ﻟﺗﺣ دﯾد ﻣﺎ إ ذا ﻛﺎ ن ھﻧﺎ ك ﺗ ﺣ ﺳ ن ﻓ ﻲ دﻗﺔ اﻟﻧ ﺻ و ص اﻟﻣﻛﺗ و ﺑ ﺔ ﻟﻠط ﻼ ب .

Having a certain degree of assessment literacy is crucial for today's language teachers. The main aim of this paper is to provide that knowledge as it pertains to the writing skill. More specifically, the purpose of this paper is to provide an overview of the main practical issues that teachers often face when evaluating the written work of their students. It will consider issues and solutions in five major areas: test design; test administration; ways to assess writing; feedback to students; and the effects on pedagogy.

The author took a very practical and principled approach to the complete process of assessing the written work of our students in a foreign or second language.

The cyclical relationship between teaching and assessment can be made entirely positive provided that the assessment is based on sound principles and procedures. Both teaching and assessment should relate to the learners' goals and very frequently to institutional goals.

Good teachers spend a lot of time ensuring that their writing assessment practices are valid and reliable. The author deals with the fundamental issues that underlie good test design in a very practical and understandable way and later suggests practical steps to ensure smooth and reliable test administration before dealing with ways to assess a range of different writing tasks. Then, the crucial issue of how best to provide useful developmental feedback to students is considered. She concludes by discussing how best testing practice should seek to accommodate the requirements of test takers.

This topic is significant as assessing foreign/second language writing skills is one of the most problematic areas in language testing. It is made even more important because good writing ability is very much sought after by higher education institutions and employers.

The purpose of the study is to look at Chinese English as a foreign language (EFL) learners’ organizational strategy use in English writing at universities in Taiwan. One significant area that has been indicated in contrastive rhetoric studies spins around the notion of culturally constructed organizational patterns. It is claimed that second language (L2) writers may have implicit culturally driven presuppositions and values about academic writing in the first language (L1) that may transfer straightforwardly to academic writing in English.

Data were from 50 high- and 50 low-achieving EFL students’ and 50 native English speakers’ (NESs’) written texts, and semi-structured interviews with EFL students and their teachers.

Based on text analysis, when high-achieving EFL students and NESs were compared, they were similar in location of thesis, existence of introduction, existence of topic sentences, macro-level patterns, existence of conclusion, existence of a concluding sentence and existence of a final comment, but different in existence of background information. Nonetheless, it is noted that low-achieving EFL students were quite different from high-achieving EFL students and NESs in several aspects, such as location of thesis, existence of introduction, existence of topic sentences, macro-level patterns, existence of conclusion, existence of a concluding sentence, and existence of a final comment. In addition, the written texts and interview findings suggest that while cultural differences do, in fact, exist, Chinese writers’ English organizational strategy use were to some extent intertwined with their writing experiences and teachers’ writing instructions. The results also suggest the flexibility of writers and multiplicity of writing experiences within a cultural group.

The study makes original recommendations for language pedagogy.

The present study investigated the effect of online collaborative project-based learning on the language performance and attitudes of intermediate level students at the College of Applied Sciences in Oman. The study is a quasi-experimental study and the sample consisted of four classes (93 students in total) in Nizwa College of Applied Sciences. These participants were in their foundation year and were taking the Academic English Course during the time of the research experiment. Two classes were assigned to the experimental group (46 students) and they were directed to collaborate online in doing their research projects. The other two classes were assigned to the control group (47 students) and did their projects individually. A language test containing reading and writing questions, an attitude questionnaire, students' project scores and transcripts of focused group discussions were used to collect the data needed to answer the research questions. The study revealed that the main positive effect of online collaborative project-based learning was clear on students' writing skill in the post test (i.e. students in the experimental group outperformed students in the control group).

This study aims to examine the effectiveness of the Central Board of Secondary Education (CBSE)-based English textbooks and question papers in developing second-language higher-order thinking skills (HOTS).

Descriptive analysis establishes a causal relationship between learning objectives and second language (L2) writing proficiency. Content analysis is used to compare and analyze tabulated data for textbooks and question papers for the English language by the National Council of Educational Research and Training (NCERT) and CBSE. This method categorizes the materials and their assessments under HOTS and lower-order thinking skills to ascertain the relationship between learning objectives and L2 writing proficiency.

The study highlights teaching material and assessment shortcomings and their alignment with learning outcomes to enhance students' writing skills. It underscores the need for HOTS-focused materials, discussing their impact on writing skills. The study also explores how textbook–question paper mismatch hampers Bloom's taxonomy-based cognitive skills.

This research illuminates the efficacy of teaching and learning English as a second language (ESL) writing skills to improve the quality of education, which has real-world implications. The study highlights flaws in the educational system in India and suggests curricular and pedagogical changes.

The research examines NCERT and CBSE ESL textbooks and question papers to align teaching and assessment methods. The results aim to improve education through ESL writers' HOTS.

The purpose of this paper is to examine how the teachers implemented innovative feedback approaches in their writing classroom and the extent to which the innovative feedback approaches impacted upon student attitude and performance in writing. In the writing classroom, teacher feedback serves as an assessment as well as a pedagogical tool to enhance the teaching and learning of writing. While there is no shortage of literature on the topic of feedback per se, there is scant research on teachers’ attempts to implement change to conventional feedback practices, as well as the impact of such feedback innovation on student learning. Drawing on data gathered from individual teacher interviews, student questionnaires, student focus group interviews, pre-and post-writing tests and classroom observations, this study seeks to explore two teachers’ change initiative in their writing feedback approaches.

The study used multiple sources of data including individual teacher interviews, student questionnaires and student focus group.

The results suggest that the innovative feedback approaches helped to enhance the motivation and writing performance of the students. The paper concludes with implications and insights to help teachers implement similar feedback innovations in their contexts.

First, the findings suggest that focused written corrective feedback is a viable option for responding to student writing, especially for low proficiency students in English as a foreign language (EFL) contexts. Second, teachers might consider the option of removal or delay in the reporting of scores, where appropriate. Third, more intensive training might be necessary to help students improve their peer evaluation skills and their ability to write more constructive comments for their peers.

The significance of the study lies in the contribution it can make to existing writing feedback research that pays insufficient attention to teacher feedback in real classroom contexts, uncovering the process through which teachers attempt to bring improvement to conventional feedback practices, as well as the impact of feedback innovation on student learning in naturally occurring classroom contexts.