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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.

The purpose of this paper is to attempt to answer whether there is a difference between retrieving memory by using recall or false recall of brands in an interactive and imagery-rich environment such as advergaming, and there are differences in memory in the same context if the languages of proficiency are based on the same script (e.g. alphabetic/alphabetic such as Spanish/English) versus cross-script (e.g. logographic/alphabetic, such as Chinese/English).

A series of international experiments addressed memory of brand placements in advergames – via correct and false recall – across groups of bilinguals from China, Mexico and South Korea.

The most salient finding of this study revealed advergame interactivity increased false memory more pronouncedly in the proficient groups (“experts”), supporting the notion of increased false recall as a result of feelings of accountability that experts naturally experience.

The procedures of the international experiments were susceptible to some limitations concerning sampling design and experimental stimuli. Despite its limitations, this study helps to uncover the effect of these elements in short-term brand memory, to guide marketers for an effective use of brand and product placements in advergames.

Analysis of both correct and false recall of bilinguals in imagery-rich environments is of utmost importance. In these environments, memory may originate from experience or from imagination. The study addressed brand memory among diverse Internet audiences by taking into account both correct memory scores as well as false memory scores within the advergaming context.

English instructors' pragmatic competence (PC) is an aspect of the overall communicative competence forming the basis of language instructors' knowledge. Their knowledge of pragmatics should not be overlooked when seeking to understand foreign language learners' communicative ability. This study aims to investigate the pragmatic awareness and teaching practices of non-native EFL instructors with different qualifications and from various cultural backgrounds in Saudi Arabia.

To obtain a broader perspective, this study adopted a quantitative research design. An online questionnaire, developed from Ivanova (2018) and Tulgar (2016), was accessed by 320 instructors at one English teaching institute in Saudi Arabia. The questionnaire consisted of demographic information about participants and 12 closed Likert-type questions.

The data analysis showed that most of the language instructors were aware of PC. However, some variations were evident in their views of the importance of pragmatics in teaching and learning and in their actual pragmatic teaching practices.

This study emphasizes the importance of pragmatic awareness for EFL instructors. It indicates that while non-native English instructors' academic levels and cumulative experience in teaching English play a major role in teaching, instructors have several challenges in teaching pragmatics and promoting students' awareness of pragmatics in this context. For effective second language teaching of pragmatics, instructors, managers and policymakers need to recognize the importance of pragmatics and competencies that students need to develop in EFL contexts.

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.

The purpose of this paper is to examine the effects of sentence combining (SC) and sentence decombining (SD) activities on fostering reading comprehension. As a widely used writing activity for enhancing syntactic fluency in English Language Arts (ELA) classes, SC requires learners to combine short sentences into longer and more complex sentences, while SD requires learners to break down a long sentence into the shortest grammatically allowable sentences.

This study assessed the effects of SD and SC in comparison with a control group on the improvement of reading comprehension ability among college students learning English as their second language (L2) in the context of a six-week English language learning program. Participants with overall intermediate English language proficiency were randomly assigned to one of three different conditions: SC, SD and control. Also, a subset of the participants was interviewed after the intervention.

The results showed that SD was more effective than SC or control condition in enhancing syntactic knowledge and reading comprehension, as measured by a standardized English proficiency test. Data obtained from post-study interviews further suggested that only SD was perceived by the participants as having enhanced their reading comprehension.

The present study provides a valuable addition to a body of research on sentence manipulation activities in ELA classes. For those L2 learners who have passed a pre-intermediate threshold level, SD appears to be more beneficial than SC in enhancing syntactic knowledge, which, in turn, appears to contribute to better reading comprehension.

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.

The purpose of this paper is to determine the degree of interrelatedness and the role of a number of context‐specific factors in the English language proficiency development of Arab college‐bound learners. These factors include: language class risk‐taking, sociability, discomfort, motivation, and attitude toward class.

The study employed a one‐group pretest‐posttest experimental design. In total, 67 (n=67) male English as a foreign language college‐bound learners participated in the study. All participants took general English language proficiency pretests and posttests in order to determine the effect size of improvement in their language proficiency after an intensive treatment of 200 contact hours. The calculated effect sizes of improvement were correlated with learners' scores on the factors under study as measured by a modified version of the Ely classroom climate measure. In addition, Pearson product‐moment correlation coefficients were computed and a step‐wise multiple regression analysis was run in order to determine the degree of interrelatedness among the variables under study and to determine their extent of their role in the effect size of the proficiency gains of the participants.

The findings indicated that language class sociability is positively related to students' motivation to learn and to a positive class attitude. Conversely, language class risk‐taking was found to be negatively related to class discomfort which in turn was negatively related to student motivation to learn. The findings also indicated that none of the affective variables under study predicted the effect size of the proficiency gains realized by learners.

The findings of this study suggest that language acquisition is a complex process determined by interaction among a number of learner‐related and contextual factors. Furthermore, the findings suggest that motivation for learning is related to learners' affective feelings and may impact their class participation. A limitation of the study is that it employed a one‐group experimental design and, as such, there was no control or comparison group.

Using humanistic/affective methods of teaching could decrease students' feelings of class discomfort and increase their motivation and class sociability.

The study provides insights into the language acquisition process of Arab college‐bound learners based on empirical evidence.

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.

This study aims to increase language learning (L2) output by incorporating a digital storytelling chatbot system (known as a “storybot”) that focused interactions on a narrative. This study also sought to investigate student perceptions of these storybot interactions and improve on poor perception rates from previous studies.

This one-sample exploratory study was of student-storybot participation rates and student perceptions towards a storybot activity designed to increase L2 output. A combination of storybot participation analytics and survey analysis of student perception was carried out.

The use of storybots in the L2 class resulted in mixed participation rates. Students read nine times more than they wrote, indicating a high degree of reading comprehension necessary for storybot interaction. Survey results revealed that students believed storybots helped them meet their L2 goals, were relevant to their L2 and were easy to navigate.

Interactions were through text messaging so no impact on speech or pronunciation could be observed. Further, the context was within a single university class in South Korea, restricting the generalization of findings to outside regions or with younger learners. Finally, while storybots proved to be valuable reading comprehension activities, the next step in this line of chatbot research should incorporate more writing prompts.

Storybots revealed explicit benefits to reading comprehension, as measured by cohesion between storybot delivered comprehension questions and student responses. Moreover, storybots can be used as examples for students in their own story creation, classroom forms to collect relevant student information regarding learning objectives and platforms for class quizzes.

Storybots scaffold students through conversations, which abide by socio-pragmatic norms, providing models for L2 learners to incorporate in real-world text-based communication. Additionally, a wide range of idiomatic expressions is contextualized in comprehensible interactions that students can learn from the storybot then practice with friends.

This study contributes to the growing research on the use of chatbots for second L2 and offers specific insight into the use of narrative storybots as a means to increase L2 output and potentially benefit L2 reading comprehension.

The purpose of this paper is to describe how dominant social practices embedded in situated report‐writing activities in an automotive discourse community in South Africa causally shape component engineers' perceptions of literacy. The study explores how the dominant practices of supervisor feedback and report acceptance causally impact on effective report‐writing perceptions during report text production.

Critical ethnography is the preferred methodology as it explores cultural orientations of local practice contexts and incorporates multiple understandings to provide a holistic understanding of the complexity of writing practices. This study focuses on data collected during two interviews and a focus group discussion with four L2 component engineers as well as the questionnaires their two L1 supervisors completed.

The engineers tended to measure or associate literacy and effective writing standards with supervisor feedback practices. These feedback practices interacted causally with the meanings or associations, the participants gave to or associated with literacy and their report‐writing competency. As a consequence, literacy was often described in terms of correct wording or terminology, grammatical correctness, spelling, sentence structures or styles in reports as determined by their supervisors during feedback practices, rather than report content, structure or technical details.

The participants constructed literacy in terms of correct language, word and spelling use and focused on linguistic errors in their report writing. They tended to perceive rhetoric and engineering discourse as separate entities rather than rhetorically constructed contextual knowledge. Language problems were usually attributed to human being inefficiencies and L1 standards rather than the individual creation of knowledge.

This paper not only impacts causally on engineering workplace writing practices but on higher education and future report‐writing practices. Digital technologies and systems will increasingly impact on report‐writing practices, what constitutes contextual knowledge and acceptable literacies as varied and different audiences define acceptable writing practices.

The paper shows that on‐the‐job writing research is limited and research that has been done often focuses on criteria for good writing as defined by experts in the field. If all workplace writing‐practice research adopts this expert view, it offers no insight and understanding into what implicitly and explicitly guides writers. Writing‐practice research also needs to focus on the voices of writers so that the influence of human social behaviour on these practices can be understood.