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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.

This study examines the relationship between the writing anxiety experienced by English second language learners and their intention to employ ChatGPT for their academic writing as an automated writing evaluation tool. This research integrates writing anxiety as an additional variable to understand how much writing anxiety affects the perceived usefulness of ChatGPT as an automated writing evaluation tool, perceived ease of use of ChatGPT, and attitude towards using ChatGPT as an automated writing evaluation tool for their academic writing with the technology acceptance model (TAM) as a theoretical framework.

This is a cross-sectional study, with SEM PLS to analysis data collected from 639 undergraduate students.

This study found that writing anxiety significantly affects perceived ease of use of ChatGPT as an automated writing evaluation tool, and attitude towards using ChatGPT. Altogether they both influence students’ intention to use the ChatGPT as an automated writing evaluation tool.

This study contributes to the understanding of students intention to use ChatGPT as an automated writing evaluation tool when they suffer from writing anxiety.

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.

This study attempts to evaluate the effect of the corpus-based inductive teaching approach with multiple academic corpora (PICA, CAEC and Oxford Corpus of Academic English) and conventional deductive teaching approach (i.e., multiple-choice items, filling the gap, matching and underlining) on learning academic collocations by Iranian advanced EFL learners (students learning English as a foreign language).

This is a quasi-experimental, quantitative and qualitative study.

The result showed the experimental group outperformed significantly compared with the control group. The experimental group also shared their perception of the advantages and disadvantages of the corpus-assisted language teaching approach.

Despite growing progress in language pedagogy, methodologies and language curriculum design, there are still many teachers who experience poor performance in their students' vocabulary, whether in comprehension or production. In Iran, for example, even though mandatory English education begins at the age of 13, which is junior and senior high school, students still have serious problems in language production and comprehension when they reach university levels.

This paper aims to show how an Academic Literacies lens can contribute to a deeper understanding of writing for a professional doctorate (PD) by focusing both on the language of supervisors’ written feedback and on student and supervisor perspectives on feedback throughout Year 1 (Y1).

Firstly, written feedback summaries on formative assessments across two Y1 cohorts on a UK PD programme were analysed thematically to identify patterns in feedback practices. Secondly, two longitudinal, detailed student/supervisor case studies were developed, drawing on multiple data sources.

Supervisors’ written feedback enacted an encouraging dialogue around assessed writing, discursively constructing a sense of solidarity on the doctoral journey, focusing on the “long view”. Case study analysis, however, revealed tensions centred around jarring discontinuities in students’ feedback experience as they transitioned from formative to summative assessment at the end of Y1.

The paper demonstrates that an Academic Literacies approach can offer valuable insights into the specific, situated context of writing for a distance learning PD and makes the case for greater attention to writing in contexts of partly taught doctorates.

Findings suggest that PD programmes should work towards providing continuity of feedback experience, through supervisor and examiner training and through assessment arrangements which support students to navigate challenging transitions between formative and summative phases of assessment.

This paper reports on an innovative research design which combined a textual “snapshot” of supervisory feedback, paying close attention to language, with detailed longitudinal case studies exploring perspectives on feedback over time. It contributes to doctoral writing research by throwing light on the relatively underexplored domain of writing in the taught phase of the PD. It contributes to doctoral education studies by highlighting the central role of feedback on writing in shaping the experience of PD researchers.

This chapter discusses how immigrants adopt translanguaging practices (i.e., the flexible use of linguistic resources by bilinguals or multilinguals to make sense of their worlds inside and outside classrooms) to scaffold learning, clarify concepts, facilitate communication, and promote academic understanding. Even inside the campus, outside the classroom discourse, translanguaging can be used to navigate bureaucratic systems, such as filling out forms, engaging in social gatherings, or accessing library services, that require proficiency in the language of the host country. Whereas the academic context is not always a positive space for translanguaging practices, everyday communications usually create a positive space for translanguaging. This chapter discusses translanguaging practices in academic and nonacademic contexts. We also provide an overview of the negative pedagogical attitudes toward translanguaging in higher education institutions, where challenges commonly faced by mobile and immobile non-native English scholars pertain to strict strategies. Reflections on the restrictive publishing policies that constitute a negative translanguaging space, acceptable and nonacceptable translanguaging practices in academic publications and the role of translanguaging facilitators, such as artificial intelligence (AI) applications, are also deliberated based on the field experience of a professional copy editor.

The study investigated the feedback seeking abilities of learners in L2 writing classrooms using ChatGPT as an automated written corrective feedback (AWCF) provider. Specifically, the research embarked on the exploration of L2 writers’ feedback seeking abilities in interacting with ChatGPT for feedback and their perceptions thereof in the new learning environment.

Three EFL learners of distinct language proficiencies and technological competences were recruited to participate in the mixed method multiple case study. The researcher used observation and in-depth interview to collect the ChatGPT prompts written by the participants and their reflections of feedback seeking in the project.

The study revealed that: (1) students with different academic profiles display varied abilities to utilize the feedback seeking strategies; (2) the significance of feedback seeking agency was agreed upon and (3) the promoting factors for the development of students’ feedback seeking abilities are the proactivity of involvement and the command of metacognitive regulatory skills.

Additionally, a conceptual model of feedback seeking in an AI-mediated learning environment was postulated. The research has its conceptual and practical implications for researchers and educators expecting to incorporate ChatGPT in teaching and learning. The research unveiled the significance and potential of using state-of-the-art technologies in education. However, since we are still in an early phase applying such tools in authentic pedagogical environments, many instructional redevelopment and rearrangement should be considered and implemented.

The work is a pioneering effort to explore learners' feedback seeking abilities in a ChatGPT-enhanced learning environment. It pointed out new directions for process-, and student-oriented research in the era of changes.

This quasi-experimental study examined the effectiveness of audio-visual and written feedback (AVF + WF) on undergraduate students versus only receiving WF in the context of an English as a Foreign Language (EFL) online classroom during the coronavirus disease 2019 (COVID-19) lockdown.

The study used the estimator Difference in Difference (DID) to compare a treated and control group in a pre-and post-test under the application of six treatment sessions, plus a student's perception survey at the end of the treatment. The treated group that received the multimodal feedback showed higher improvement rates in the paragraph content between the first and final drafts than students in the control group.

Results indicated that receiving a combination of AVF + WF had a statistically significant effect on mechanics (p < 0.001) and the use of transition words (p = 0.003).

These findings will benefit educational agents, professors and stakeholders for social and economic development.

While previous studies have only used student perceptions of the feedback, this study contributes with empirical data through quasi-experimental analysis and measures the effectiveness of feedback in online learning environments.

This study investigates the potential effectiveness of ChatGPT in enhancing the academic writing skills of Saudi EFL undergraduate students. It also examines the challenges associated with its use and suggests effective ways to address them in the education sector.

The study employed a sequential mixed-methods approach, which involved distributing questionnaires to gather data from students, followed by conducting semi-structured interviews with a purposeful selection of eight students and six teachers.

The findings revealed that students were generally satisfied with the effectiveness of ChatGPT in enhancing their academic writing skills. However, they also pinpointed some challenges associated with using ChatGPT, including plagiarism, overreliance, inadequate documentation, threats to academic integrity, and inaccurate information. To alleviate these challenges, effective strategies include deploying detection tools, equipping students and educators with training sessions, and revisiting academic policies and assessment methods. It is recommended that ChatGPT be used responsibly as an assistant tool, in conjunction with students' ideas and teachers' feedback. This approach can significantly enhance students' writing skills and facilitate completing their research projects and assignments.

ChatGPT can be a valuable tool in the educational landscape, but it is essential to use it judiciously. Therefore, teachers' effective integration of ChatGPT into their classrooms can significantly enhance students' writing abilities and streamline their research process.

This study contributes to recent AI-based research and provides practical insights on the responsible integration of ChatGPT into education while addressing potential challenges.

This study aims to investigate the potential effect of project-based learning (PBL) in alleviating the negative psychological factors that hinder language learning in an intensive-English course at a private Lebanese university through its meaningful, student-centered, collaborative, and creative pedagogical approach and supporting students in constructing a positive self-identity as an English language learner and user.

The L2 Motivation Self System theory describes the roles of the Ideal L2 Self, the Ought to L2 Self, and the L2 Learning Experience as the three dimensions that trigger L2 motivation, which is an important factor in achieving L2 learning outcomes. This pre- and post-test questionnaire study aims to investigate the impact of designing and implementing project-based learning as a creative pedagogical method on the L2 Learning Experience of tertiary EFL students undertaking an intensive English language course.

The paper found that such a pedagogical approach decreased students’ foreign language anxiety and positively impacted their attitudes toward L2 learning as they actively engaged in their projects in a meaningful, connected, and creative manner. This paper concludes that the adopted innovative pedagogical approach created an L2 language learning experience for EFL students to move closer from their actual self to their potential or imagined Ideal L2 Self as they embraced their creative self, social self, and risk-taking self within the frame of identity negotiation and construction. Findings from this study map to the wider literature on L2 language acquisition and foreign language learning.

This study has theoretical and practical implications. Theoretically, this research contributed to the existing body of literature on L2 education from a nuanced lens that maps students’ motivation, engagement, characteristics, and psychology to their identity as L2 learners to make their learning journeys more rewarding, productive, and connected.

Practically, this study showcases how the implementation of the PBL approach in a creative and aligned manner can positively impact students’ attitudes toward English teaching and learning, making novel reading a more engaging task that prompts students’ creativity, critical thinking, and risk-taking while facilitating the negotiation and construction of their identity as L2 learners and users. Additionally, the implementation of such creative pedagogical approaches prompts on an educational policy level the shift from the traditional ways of English teaching and learning to a more student-centered, engaging and connected state, creating a set of exemplars for EFL and ESL instructors to use to ensure that L2 learners connect to their own experiences and develop a range of transferrable skills while acquiring language capabilities.

This paper concludes that the adopted innovative pedagogical approach served as a channel for EFL students to move closer from their actual self to their potential or imagined Ideal L2 Self as they embraced their creative self, social self, and risk-taking self within the frame of identity negotiation and construction. Findings from this study map to the wider literature on L2 language acquisition and foreign language learning.