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Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

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.

The purpose of this paper is to explore the explicit teaching of information text schema with vocabulary instruction to primary-aged students in Hong Kong international education.

Data were collected through three quasi-experimental studies with different age groups and participants. Each study divided participants into two randomly assigned groups, either informational texts (IT) or vocabulary building (VB). Impact was evaluated with gain scores on a standardized reading comprehension test and researcher-designed cloze tests of fiction and nonfiction passages.

The explicit teaching of IT can benefit student reading comprehension from an early age, particularly to first language (L1) English students and possibly second language (L2) English learners. School reading programmes should include opportunities for students to experience IT (nonfiction) and fiction materials, and build their vocabulary through incidental learning and explicit teaching. For IT, they should be exposed to: layout – e.g., headings, sub-headings, glossary, and index; and content – photographs and specific/technical vocabulary. For fiction-based texts and VB, the following themes should be covered by younger aged students: antonyms, synonyms, and affixes.

Several limitations apply to this study which will need to be addressed in future studies. These include: the random sampling of students from the overall student population was not an option, given the necessity of voluntary participation and avoiding disruption to school routines. This study used meta-analysis to aggregate results across multiple comparisons largely because of the extremely small samples available. The data show large standard errors as a consequence of small numbers of participants. Hence, the current results, notwithstanding the power of meta-analysis, need to be validated with much larger samples in future studies.

This paper suggests that greater comprehension and cloze performance among L1 students was found due to the teaching of IT compared to vocabulary training, with the reverse result for L2 English learners.

The purpose of this study was to investigate the effects of word-related factors (i.e. frequency, range, dispersion and cognateness) on first-language English (L1E) readers' (n = 20) and second-language English (L2E) readers' (n = 20) incidental acquisition of vocabulary through the reading of an authentic novel.

Participants read A Clockwork Orange by Anthony Burgess, a 58,686 token (word) English language novel containing Slovos, that is, words from Nadsat, a futuristic, foreignized teen talk invented by Burgess. Upon finishing the novel, the participants took two unexpected vocabulary tests, one for meaning recognition and the other for meaning recall.

The results of this study indicate that word-related factors significantly correlate with the word meaning recall test scores of both groups. However, the regression models of meaning recall for the two groups showed that dispersion was the most robust predictor, which implies that the participants recalled more word meanings when the novel had a more even distribution of the unknown target words. The meaning recognition test scores showed cognates were a significant predictor for the L1E readers but not for L2E readers.

This study marks the first attempt in the field to investigate the relative contribution of frequency, range and dispersion – a closely bound set of word-related factors – to both L1E and L2E readers' incidental acquisition of vocabulary through reading an authentic novel. Considering the important role of dispersion, the current study suggests that developers of graded readers and children's literature should more evenly distribute unknown target words in their books. Doing so will better facilitate both L1E and L2E readers' acquisition of those words. The study also addresses a fallacy of methodology regarding incidental vocabulary acquisition by examining the effect of the cognateness of the foreignized words embedded in A Clockwork Orange. The L1E readers' sensitivity to cognates implies that cognate-word awareness-raising activities are necessary to learning a foreign language, especially if that language has many cognates in common with English, such as Spanish.

Based on an experimental study on English for Specific Purposes (ESP) students, at the Business Department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well.

The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive.

The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group.

The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development.

Examining and exploring whether the use of chatbots plays a major role in enhancing and learning ESP vocabulary in English as Foreign Language setting.

The purpose of this paper is to examine the effectiveness of using colors and learner’s intelligence quotient (IQ) in teaching new vocabulary in Arabic (L1) and English (L2) to children with autism spectrum disorder (henceforth, ASD).

To this end, 12 autistic children whose ages ranged between 7 and 12 were observed while they were being taught ten new words. The children were divided into two groups based on their IQ: Low (70–74) and High (76–79). The children were also divided into two groups: Group 1 studied the words written in a black font, whereas Group 2 studied the same words, but written in colors (each letter in the word in a different color), and an illustrative picture was provided with each word for both groups. The pictures were also different in color in the former group, while the picture was in black and white in the latter. The children involved in the study have a relatively slight ability to read letters based on an annual language assessment conducted by the center, and they learn a new word by learning its shape and by repetition. The experiment took place over a two-week period that involved teaching, revising and testing.

The results of the study showed that the children’s IQ played a crucial role in learning L1 and L2 vocabulary. The results also demonstrated that using colors had no significant impact on the children’s performance in the test. Finally, the results showed that teaching new words to children with ASD through repetition and drilling could be regarded as a useful technique. The study concludes with some recommendations for further studies.

The study shows that using pictures is a very useful tool in teaching L1 and L2 vocabulary to children with ASD.

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.

In this chapter, the authors provide an introspective account of how teachers in mainstream classrooms can use questioning to more effectively differentiate literacy instruction for English learners across subject areas. The authors offer a rationale for constructively responsive questioning and share tools and strategies for adapting levels of questioning to students’ English proficiency and grade levels with the goal of strengthening instruction and promoting student engagement in learning.

This research shows how social media has affected learning at present during the COVID-19 pandemic and how it has become the largest and most convenient area of communication. In the current scenario, it seems that social networking sites not only had a profound impact on our social structure and intra-social interaction, but also affected education in general and learning English language in particular. It has been proven that these various social media platforms have created a realm of digital environment in today's new-age learning. Social media platforms are social networking sites through which people interact and communicate with each other easily and conveniently. Undoubtedly and unquestionably, social networking has been proven to be a global phenomenon that has caused a vast paradigm shift in the world of Learning and education during the current pandemic. Therefore, the present study aims to reach the extent of the impact of the various social media platforms on learning English language during the COVID-19 pandemic from the students' point of view.

The study was conducted at the undergraduate level for English language learners. The sample comprised 166 undergraduate students at Najran University. A survey questionnaire was administered to find out the impact of various social media platforms and social networking sites on learning English language in the academic year of 2020 due to the COVID-19 pandemic and to highlight possible suggestions for improving future virtual language learning.

The findings of the study contributed to the area of online learning of English language during the COVID-19 pandemic. Final results confirmed that the utilization of social media has been significantly perceived to have positively impacted learning English language in terms of writing style, reading skills, listening and lexical variation, communication skills and grammar usage.

The findings of the study can serve as fundamental indicators to implement prompt pedagogical reformations, for which a number of pedagogical implications can be proposed. Another equally important pedagogical implication is to design and provide professional development and training sessions to both students and educators on the ultimate utilization of social media as instructional technologies in the context of English language teaching and learning.

This research provides insights in developing policies to assist with the integration and utilization of social media platforms as instructional technologies in the context of English language teaching and learning and how institutions can respond to the advent of advancing technology, especially during and after the COVID-19 era. A model to improve online English language learning process is recommended as a guideline for all educators offering online learning.

With an increasing emphasis on the reading development of L2 learners of English and a growing body of literature on L2 reading, it is now time to examine what the current research on L2 reading says about L2 learners’ reading development and to discuss what would be a desirable future for L2 reading studies. Focusing on the L2 reading of upper elementary, middle and high school students in L1 settings, this study aims to carefully, but critically, explore the major research studies published in the past three decades. In particular, it uses sociocultural and critical frameworks that view language as a social phenomenon and literacy as a constellation of socially contextualized practices to explore the issue of L2 reading.

To identify key findings about L2 reading, a systematic literature review of studies examining L2 reading in L1 settings was conducted. A critical examination and analysis of 91 studies on L2 reading for upper elementary students (Grades 4-12) are presented here. Based on the literature review, the major issues addressed in the previous section are revisited, and the requirements of future research on L2 reading are discussed.

Three major changes have taken place in L2 reading studies: from monolingual/L1-based research to multilingual/L2-based research; developing the socially situated model of literacy (literacies); and adopting a sociocultural and critical lens: L2 reading and L2 reading assessment. Based on the critical review of the major research studies published in the past three decades, this paper identifies the research and approach required to advance the field of L2 reading: the continua of L1 and L2 reading, macro–micro analysis of L2 reading context and diversification of L2 reading research.

Based on a systematic literature review, it demonstrates the current trends in L2 reading research, to examine the key findings and implications, and to identify what additional research or paradigms are required to advance the field. The literature review presented in this paper helps language educators, policy-makers and school administers at all levels in both first-and second-language contexts to better understand the rapidly increasing number of L2 English learners in L1 classroom settings.