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This study examines how English teachers use culturally relevant pedagogy (CRP) to support postsecondary readiness for underrepresented students in the context of dual credit (DC) coursework in the USA. Postsecondary readiness, termed “college readiness” in the USA, refers to the skills and knowledge students need to succeed at a university. DC courses are university-level classes delivered to high school students through partnerships with postsecondary institutions, most often two-year community colleges. The purpose of this study is to highlight practices and institutional conditions that enable English instructors to foster postsecondary opportunity for all.

Using an interpretive approach, this qualitative study analyzes data derived from in-depth interviews with five community college English instructors who teach DC to diverse high-school students and who apply CRP in their classroom practice.

Findings reveal that instructors used culturally relevant approaches not only to help students access dominant college-ready skills, but also to reimagine what constitutes college readiness to begin with. Instructors also took advantage of their unique positioning as postsecondary instructors working with secondary students, leaning on academic freedom to push boundaries with their curriculum.

This study shows how English instructors are uniquely positioned to enhance university preparation and build a more inclusive vision of postsecondary readiness for all students. The study also highlights institutional conditions, such as teacher autonomy, pedagogical training and administrator support, that can promote culturally relevant postsecondary preparation in English classrooms.

The chapter discusses how adolescents are moving beyond the dichotomy of biological and linguistic socialization, forming interpretive meanings at home through the reading of literature in their mother tongue, Bengali. Involving cultural relevance and non-vulnerability, the chapter conceptualizes “leisure activities” and “leisure pursuits” of reading practice of the IXth and Xth graders from both Bengali and English medium schools in Kolkata. The discussion from the theoretical construction mentions the further conceptualization of reading habits and language choice. This is where adolescents derive their agency. Adolescents from the Indian and especially from the Bengali perspective have a path of colonial discourse. From historical standpoint, the change in Bengali language and its grammar structure has influenced the acceptance of Bengali literature among adolescents in varying degrees through generations. Using mixed methods and content analysis, the chapter focuses on young teenagers’ narration on the way they maneuver curriculum and literature in their respective homes. Authors, for example, Sunil Gangopadhyay, Satyajit Ray, and Bibhutibhushan Bandyopadhyay, form the Bengali identity construction in the present time. Rabindranath Tagore’s, Sarat Chandra Chattopadhyay’s and Bankim Chandra Chatterjee’s works are always prevalent in the Bengali language syllabus. These are considered the foundational modern literary figures of pre-independent India. These are taught from a nationalist and gender discourse perspective. The adolescents in this chapter also read those at a minimum level at home and attempt to juggle the difficult vocabulary involved. The simple language of post-independent literature is much sought after by teenagers compared to pre-independent literature. Sunil Gangopadhyay’s Kakababu series, Satyajit Ray’s Feluda and Professor Shanku series, and Bibhutibhushan Bandopadhyay’s Chander Pahar stand out among the adolescents from both English and Bengali medium unanimously in this chapter.

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 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.

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.

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 aims to explore motives behind teachers' and students' use of translanguaging and how they use it in Tanzanian public secondary school classrooms.

Data were collected using interviews and non-participant observations.

The findings indicate that translanguaging was used to facilitate content comprehension, promote classroom interaction and increase students' motivation to learn. Translanguaging was implemented using three strategies: paraphrasing an English text into Kiswahili, translating an English text into its Kiswahili equivalent and word-level translanguaging.

By highlighting the motivations for translanguaging and corresponding strategies associated with translanguaging pedagogy in the Tanzanian context, this study has significant practical implications for teachers and students to showcase their linguistic and multimodal knowledge, while fostering a safe learning space that relates to students' daily experiences.

The study offers new insights into previous research on the role of language-supportive pedagogy appropriate for teachers and students working within bi-/multilingual education settings.

While many works have reported adopting exploratory practice (EP) principles in language teaching research, only a few studies have explored the enactment of EP in an online extensive reading of students majoring in English education. Given the relative paucity of attention to the use of EP as the practitioner research in English language teaching (ELT), the present EP investigates how students understand online extensive reading practice mediated by online group discussion and extensive reading logs, where the first author served as the online extensive reading practice instructor.

The exploratory practice focuses on incorporating research into pedagogy and fastens the importance of the quality-of-life in the classroom. The data were collected through students reading logs and semi-structured interviews. The collected data were analyzed using the thematic analysis. In this case, there were six phases including familiarizing with the data, generating initial codes, searching for the themes, reviewing the themes, defining the theme and writing up.

The findings reveal that online group work driven by EP enables everybody to engage in learning activities. EP assists the students in perceiving their potential and gaining a better awareness of the need to devote themselves to the class. In the EP activities, they work together to build a peaceful situation to advance the quality of learning in EFL classrooms.

The present study’s limitation is the small sample. Apart from that, the research results cannot be generalized to other places.

This study suggests that EP is suitable to create a mutual understanding among the learners and teachers. To conclude, English language competency can be achieved in a pleasant atmosphere through EP.

The present study succeeded in adding new literature studies related to EPs by discussing online group discussions and their challenges during the learning process. These aspects were identified through reading logs and interviews with students. Thus, it focuses on the implementation and challenges of online group discussions.

Supporting students transitioning from high school into college continues to be a challenge for academics and policy-makers. Composition assignments that include Kuh’s (2008) High Impact Practices (HIP) and the Association of American Colleges and Universities (AAC&U) rubric and HIP tenets of Civic Learning and Community Engagement (Fig. 1), help foster opportunities for empathy, which develops students’ abilities to think critically, write well, and succeed in college and beyond. While effective college teaching and instruction are necessary, increasing enrollments, and increasing percentages of First-Year Composition (FYC) students requiring supportive composition courses compound the difficulties of the effort. According to AAC&U, “a global community requires a more informed, engaged, and socially responsible citizenry” (2009, p. 1; Finley & McNair, 2013). In other words, educators and employers believe that “personal and social responsibility should be core elements of a 21^st-century education” (AAC&U, 2009, p. 1). This conceptual content analysis study framed by HIP analyzed 10 FYC syllabi from different composition faculty at one urban Hispanic public four-year university (SMU) in Southern California during the 2015–2016 academic year in the context of the university’s mission statement embracing Civic Learning and Community Engagement for FYC students.