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COVID-19’s attack on the system of education left the learning of many students in limbo. For minimizing this effect, the current study sought to probe how Mobile-Assisted Language Learning (MALL) can facilitate English as a Foreign Language (EFL) learners’ vocabulary knowledge. In so doing, a quasi-experimental pretest-posttest research design was adopted to check the effect of a newly developed application (i.e. visual vocabulary learning, hereafter VVL) in an EFL classroom.

To complying with the objective, a number of intermediate students (N = 40) were considered to participate in the study. They were assigned into a visual learning group (VLG) and a nonvisual learning group (NVLG) comprising 20 students in each. The VLG was directed to learn vocabulary by a VVL, and the NVG, a conventional method was employed for vocabulary instruction. An independent samples t-test was run to screen the efficacy of the intervention.

The finding revealed that participants in the VLG outperformed significantly in the posttest. The finding is beneficial for EFL teachers, syllabus designers, and material developers to take advantage of mobile applications in teaching vocabulary.

This study was conducted during COVID 19 between two groups in Iran.

Many students are found facing difficulties in learning English due to plenty of reasons: known and unknown ones. In order to overcome such an issue, the instructors have to explore for effective techniques of teaching English to motivate learners by any means. Technology in general and informatics in particular can be thought of as innovative tool of pedagogy in the current teaching-learning environment. The present proposal of research aims at creating innovative approaches, which attract learners' interest and catch their attention for better outputs.

Following subsections have been discussed keeping the significance in view. Setting of the study: The present study was conducted at King Abdulaziz University, Jeddah-Saudi Arabia, which is one of the Saudi Arabian universities; however, it has opened up new avenues for the pedagogues, teachers of English and researchers to conduct studies in various allied fields. In order to have a representative sample of students' population, the participants were chosen from the “preparatory year”. Material and tools: This study used a questionnaire (Appendix) and a test to evaluate performances of the two groups: controlled and experimental. In order to further strengthen the findings, semi structured interview was conducted for a few select students from the student-sample. Since the questionnaire was adapted, the statistical validity and reliability was not considered to be essential; however, content/face validity was ensured by consulting 10 experts in the field of education/methods of teaching.

Based on the analysis of data gathered from the test performances of the two groups of students, it was found that there existed a significant difference in the test scores. The questionnaire responses also proved that infographics can be proved to be an interesting tool of education in general and English language teaching in particular. However, it has been noticed from the gathered data that not many teachers are comfortable using multimedia or infographics for different reasons. The results of the present study are in line with the study by Rezaei and Sayadian (2015) that support the idea of infographics that would help English teachers to develop understanding and insights to design among the learners. They further contend that the infographic instruction offers choices for the utilization of collaborative activities. In addition, the infographics enhance students' performance as a whole as also supported by Alrwele (2017).

The study was conducted on a small sample which might affect the generalization of the outcomes. It was carried out with special reference to teaching of vocabulary and reading.

There can be many recommendations for different stakeholders. For teachers, it is recommended that they should know about the significance of infographs for catching the attention of students. They should know how to design interesting and colorful infographs. The administration/management should facilitate the teachers with the required software or platform to create infographics and integrate in an English language class. In addition, teachers should attend workshops and training courses organized for topics related to the infographs.

The author checked the study for plagiarism (excluding references) and found it to be 93% plagiarism free.

The purpose of this chapter is to identify specific instructional strategies to help English language learners develop literacy skills. Potential difficulties in areas of decoding, vocabulary, and fluency are explored along with suggestions to implement effective instruction. The intention of this chapter is not to be a research document, but a pragmatic guide for educators of English language learners. Through reflective practice and backed by research, I walk readers through classroom and professional development scenarios and also present ways to effectively support the emerging literacy skills of English language learners. Readers will be presented with research-based instructional methods shown to enhance crucial early literacy skills for English language learners along with practical suggestions for teachers to put research into practice in the classroom. Scenarios and research-based practices illuminate how to effectively work with English language learners. Research-based evidence is presented, showing that English language learners go through the same developmental milestones as native English-speaking students, but may require some additional modifications along with explicit instruction. The chapter describes how teachers can build foundational reading skills for English language learners, something that is crucial for later academic success.

This chapter examines factors impacting vocabulary development in preschool dual language learners, providing a cultural and linguistic perspective on vocabulary instruction in this population. Through a multidisciplinary review of the research literature, instructional strategies that can support vocabulary development in this population are identified. The chapter concludes with a detailed illustration of how these strategies can be incorporated into a culturally linguistically responsive vocabulary approach for Latino preschoolers.

Web-based language learning (WBLL) materials have long been favored by English language instructors because they are plentiful, easily accessible, user-friendly and, most importantly, free. This research looks into the effects of learning the simple perfect tense translation in three different English translation classes that used three different teaching methods: traditional face-to-face, integrative and web-based learning. It also aims to investigate the impact of gender on every mode of instruction and to identify the most effective method for learning translation.

The study is quantitative, with a pretest/posttest quasi-experimental research design. A total of 93 third-year undergraduate students (51 female and 42 male students) participated in the pretest/posttest design. Each group was exposed to one mode of instruction for nine weeks. All groups sat for a pretest in the first week of the treatment. After the treatment, the participants were provided with a posttest, and the data obtained were analyzed using the SPSS computer software program.

The findings revealed a significant difference in both tests for all modes used. All three groups improved in their gain score, but the highest gain among these groups was the integrative method, followed by web-based learning. The result of the independent sample t-tests and ANOVA exhibited that there was no significant difference in the level of students between the two groups, both were sig. two-tailed (p = 0.342). Furthermore, it was discovered that gender did not affect students' performance in the posttest (Z = −1.564, p > 0.05) when each mode of instruction was applied. Finally, the integrative method was observed to be the most effective.

The findings can inspire translation course designers to plan necessary policies or syllabi regarding English translation courses and may serve as a platform for improving the curriculum for training and motivating the next generation of English as a Foreign Language (EFL) learners.

The findings of this study could be used to improve English instruction in countries where English is a second or foreign language. The actual gap in knowledge is that no other studies have compared all three groups in the past few years.

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 paper aims to investigate the impact of blogs on teaching English as a foreign language (EFL) to primary students. The study also explores educators’ perceptions of social media tools and digital literacy in school environments.

A three-step approach was used to collect both qualitative and quantitative data. First, the blogs of 23 fifth-grade students in an EFL class in Kuwait was analysed for one semester along with student perceptions and teacher observations. Second, a survey was distributed to the students at the end of the semester, and finally, four focus groups were conducted regarding educators’ perceptions on the use of social media, their skills and needs and problems encountered.

The findings showed that by the end of the semester, students were writing lengthy sentences, had fewer spelling and grammatical mistakes, were more motivated and independent and displayed a more positive attitude towards learning EFL. In addition, educators are enthusiastic about using Web technologies in their teaching practices but have several concerns such as digital literacy competencies, technology-use behaviour and lack of accessibility to digital content.

The number of questions on the student survey could be reduced. Also, it would be preferable in future studies to require all students to blog at least twice a week which could provide a broader representation of their writing abilities.

Little research has been reported in the literature on the use of blogs in teaching EFL to primary school students. Most literature focuses on teaching EFL to university students. By using a mixed-methodology approach, this study also highlights the needs of educators and students in technology learning environments and makes a good contribution towards offering pragmatic solutions.

Based on consideration of learner needs for expanding vocabulary and the complexity of educational content, this paper introduces a model aimed at facilitating English vocabulary learning.

By measuring a set of effective variables regarding simplicity of English sentences, a ranking algorithm is presented in the proposed model. According to this ranking, the simplest sentence in the recommender system (RS) is selected and recommended to the user. Furthermore, Pearson correlation coefficient was used for checking the degree of correlation among the respective parameters on sentence simplicity. For evaluating the efficiency of the recommended algorithm, a prototype was designed by programming using Embarcadero Delphi XE2.

The results of the study indicated that the correlation among the parameters of word frequency, sentence length and average dependency distance were 0.723, 0.683 and 0.589, respectively. The computed final score is considered to be more accurate.

The application of RS in language learning and education sheds light on the theoretical validity of system thinking by highlighting its key features: its multidisciplinary nature, complexity, dynamicity and the interdependence and relation of micro and macro levels in a system.

The proposed method has significant pedagogical implications; it can be used by second language teachers and learners for checking the degree of complexity/learnability of discourse and text.

This paper proposes an alternate model with a significantly higher speed for computing final sentence score.

The purpose of this paper is to combine conceptual and documentary research.

Drawing on a range of New Zealand curriculum documents and on the history of English subject in the New Zealand context, it maps aspects of the contestation that has accompanied the development of various versions of the subject over time. It also explores ways in which the subject has always drawn on a range of primary disciplinary discourses through a process of recontextualization (Bernstein, 2000).

Based on this analysis, it problematizes the conventional location of literary study within the English curriculum, arguing that this arrangement disadvantages English as an additional language (EAL) students with an interest in literature. As another plank in the argument, it argues that literary study is itself currently disadvantaged by being linked to narrowly conceived notions of textual practice and the pervasive power of high-stake assessment technologies in constructing content and pedagogy.

A solution to both problems is offered, arguing a case for relocating literary study in an expanded Arts curriculum. The paper then goes on to draw on the concept of disciplinary literacy, to argue a case for the “reinvention” of the English teacher as a cross-disciplinary resource teaching a re-framed subject renamed “Disciplinary Rhetorics”. It concludes by discussing the implications of these two re-envisionments for English teacher identities and the construction of their professional content and pedagogical knowledge.