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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.

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 aims to explore computer-assisted language learning (CALL) activities to improve the English essay writing of Pakistani university students. It highlights the role of CALL in the language proficiency model cognitive academic language proficiency (CALP), which can enable second language learners to engage in more critical thinking skills effectively. Furthermore, it aims to explore English language teachers’ experience about the use of CALL-based activities in English language classrooms or labs.

This study followed a pre-post study design to examine whether the structure of an essay and computer-based activities helped to improve English language proficiency among non-native speakers. This design is adopted to investigate the effects of CALL activities on students’ English essays. This study used data from 198 students and after this, four English teachers were interviewed to know their opinions and observations about these CALL-based activities and students’ essay writings. Pre- and post-test data was analysed in percentages and further examined through a t-test. The findings of teachers’ interviews were further analysed through Nvivo software to develop an understanding of research questions through significant themes.

The pre-test results confirmed that students’ language proficiency is underdeveloped and informal. Students needed help constructing introductory paragraphs with the reader’s hook, background information, three-point thesis statement and transitional hook. Whereas, post-test results showed that students’ English language proficiency in essay writing was found improved with the use of technological tools and CALL-based activities. Furthermore, it was observed that structure and teacher feedback in essay writing are vital for English proficiency.

This study benefits English language teachers, administrators, language policymakers and syllabus designers at colleges and universities. The debate over how to improve English language proficiency and academic achievement presents diverse challenges across several countries. Non-native speakers, at an undergraduate level, can significantly improve academically and become proficient in English essay writing with the help of structural guidelines and the use of educational technology. This article demonstrates a two-way process to improve an objective definition of English language development, which is conceivable if the technology is adopted.

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 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 aim of this study was to examine whether, when a person reads, there is a boost to all material related to the context in Najdi Arabic (NA) as was found in English in was found in Rodd et al. (2013). The study employs Arabic language features of orthographic style and diglossia to answer the research question.

Forty-two participants were asked to come on two sessions to complete a reading task, a filler task and a word recognition task. The word recognition task included 14 homographs that could mean one thing in Modern Standard Arabic and another in NA.

The findings show that the assumption that all related materials are ready to be used when readers are exposed to the context is not valid for the two Arabic variants.

One limitation of this study was that the participants were all female.

The findings could help writers write better texts to help individuals who struggle with reading comprehension whether it is because of dyslexia, Attention Deficit Disorder (ADD) or Attention Deficit Hyperactivity Disorder (ADHD) as when researchers understand how priming works, they might be able to help readers in their reading fluency and comprehension (Rodd et al., 2016). This could be by producing better texts to comprehend or using semantic priming in classroom setups.

By engaging levels of W/writerliness, this paper aims to identify how English Language Arts teachers’ personal and professional W/writerly identities impact their performance of pedagogical agency.

In this narrative inquiry, the author draws on theories of writing identity and agency to analyze how four mid-career English teachers’ personal beliefs around writing intersect with their professional practice. Data sources include interviews, journal entries and classroom observations.

Nuanced differences in teachers’ W/writerly identities produce more substantial differences in their pedagogy, especially impacting their performance of agency to (re)define successful writing outcomes and to balance process and product in their writing instruction.

This paper presents one method to expand preservice and in-service English Language Arts (ELA) practitioners’ approaches to teaching writing even alongside limitations of their teaching context by (1) emphasizing their ownership over their own writing in university methods courses; (2) leading teachers on an exploration of W/writerly identities; and (3) investigating ways teachers can transfer their personal and professional learning to students via their own pedagogical agency.

The study extends the work of scholars in the National Writing Project, suggesting that nuanced exploration of ELA teachers’ W/writerly identities in preservice and in-service settings could increase their sense of agency to work against and within cultures of standardization.

This study set out to investigate PhD students’ perceptions of the criteria for showcasing English proficiency as part of the requirements for completing PhD programs in Iranian universities. The criteria included The Ministry of Science, Research, and Technology (MSRT) test, an English for Academic Purposes (EAP) program, achieving the minimum score on the English section of the doctoral entrance exam, and gaining approval from supervisors regarding students’ proficiency.

Qualitative data were collected by conducting interviews with 19 PhD students as well as a corpus of 325 students’ commentaries posted on an online forum. The data were then analyzed using the constant comparison method (Creswell, 2013).

Students held mixed feelings in relation to the new completion requirements and in particular, the MSRT test. However, they advocated egalitarian, objective, and need-responsive measures, which were better mirrored in the EAP program.

The findings were discussed with reference to the predictive validity, authenticity, and construct relevance of the MSRT test and the EAP program. Despite the absence of quantitative data on the comparability of high-stakes tests and the EAP program, the study offers implications for testing agencies, academic institutions, and tertiary students.

A multitude of PhD students and candidates are affected by the results of the new policy. Accordingly, gaining a better understanding of students’ perceptions may assist policymakers in reconsidering their policies, if necessary.

In November 2022, the commercial company, OpenAI, launched ChatGPT. Since then, university students have rapidly become regular users of this artificial intelligence (AI) platform. One reason for this is the powerful capability of this generative AI tool to produce textual content, which in many cases, is almost indistinguishable from human-generated content. Another reason is that ChatGPT easily gives anyone access to knowledge. However, there is a problem as the vast majority of its users have no idea how this AI platform works and thus overlook the importance of thinking critically about information communicated in ChatGPT. While some call for banning this generative AI tool, this study aims to provide evidence that science classrooms can become scenarios where students find explicit, concrete, and realistic opportunities to critically evaluate scientific information generated by ChatGPT.

An intervention study was conducted with 55 students (26 females and 29 males, 17–24 years old) during a university Spanish-English bilingual science course taught within an active learning environment. The data consist of the written critiques of the students about Spanish-English bilingual scientific texts produced by ChatGPT.

Results indicate that the intervention had a positive effect on students’ abilities to construct sound arguments in Spanish and in English while judging the quality of scientific texts produced by this AI bot. Moreover, the findings suggest that the intervention enriched students’ skills to make improvements to texts produced by this generative AI tool.

The evidence provided in this study contributes to the exploration of possibilities to help students become critical users of ChatGPT.

The paper’s aim is to explore female academics’ publication aspirations and constraints in a less researched area of Central and Eastern Europe (CEE).

The research data presented in this paper is part of a larger cross-European project involving semi-structured interviews conducted with Early Career Researchers (ECR) (PhD+ 8 years) from Social Sciences and Humanities (SSH) from seventeen European countries.

The findings show how ECR women from the CEE region in SSH encounter difficulties when trying to publish, which obstacles add to a strong economic and geopolitical dependence. Findings reveal that female ECRs use various publications strategies to enhance their scientific career and engage in the neoliberal model of academia, but mostly stay in their local scientific communities, without building internationally recognized scientific careers. Thus, they do wish for a global recognition, but they opt for a safer and more accessible choice of publishing in their local scientific communities.

Knowledge which academic women from the CEE region produce (mostly in their local languages) stays in their local and isolated enviroments, creating an imbalanced knowledge advancement in a international academic arena which recognizes only publications written in English and in renowed journals.