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The purpose of this paper is to determine the level of intrinsic motivation of primary-school-age children alongside the factors that influence these levels when learning English.

This goal was reached through a study that was conducted in four educational establishments of Tyumen. The study benefits from qualitative and quantitative methods. The qualitative part consists of an experiment in a group setting. Two groups of students were learning under two different programmes and the teachers were making records of student outcomes, interest in learning and motivation. The findings demonstrate that the level of motivation/interest is higher when interactive techniques (appropriate for the age of students) are in use. The quantitative part involved a survey to identify intrinsic motivations by completing which the students revealed high and medium levels of motivation/interest to learn.

The findings can be used when updating or re-designing education programmes and when creating new methods for teaching English in Russian educational establishments.

Giving the schoolchildren a motivation to learn is, without any exaggeration, one of the central problems in modern school. Teaching English as a foreign language to students of younger age (schoolchildren) requires a special approach due to special psychological and mental characteristics that these students have. The scholars have established that learning of foreign languages happens best at a very young age. However, without proper methods of teaching, teachers will not be able to reach the learning objectives, which they were attempting to reach. The reason for this effect is simple. The way the subject is taught is expected to spark interest but with the lack of interest in the subject, students will not feel sufficiently motivated to actually learn something. Hence, motivation is essential for learning any foreign language. In the home setting, motivation to learn, as well as a positive learning environment, is the responsibility of parents.

The purpose of this study is to examine the relationship between the understandability of an accounting textbooks written in English and the language learning motivation of international students. Previous research assumed that native speakers of a language and second-language speakers would understand a given accounting text similarly and little attempt has been made to ascertain any individual differences in users’ capacity to read and understand a foreign language.

The 107 participants in this study comprised of full-time English as a Second Language postgraduate commerce students studying at a major Australian university. The authors used two-part questionnaire to examine the motivation of participants and the understandability of an accounting textbook using the Cloze test.

The results suggest that most international students have difficulty in understanding the textbook narratives used in this study. Furthermore, the results show that students’ motivation to learn a foreign language impacts on the understandability of an accounting textbook.

This study will help the educators, textbook publishers and students to understand the needs of ESL students. It is expected to provide guidance for authors and instructors to enhance the effectiveness of the accounting courses.

The accounting literature shows that there have been efforts by accounting researchers to measure the understandability of accounting texts or narratives. This research provided valuable insights of the learning challenges of international students and valuable recommendations to educators and publishers to enhance the delivery.

With the growing use of technology in second language learning (L2), many techniques of incorporating digital video in L2 learning and platforms of task implementation appear in the field, however, with little, if any, research on how tasks can be designed and developed in these contexts. Based on Chapelle (2001, 2014) task design criteria, the current paper evaluates specifically the “interactivity” of task design interface and how it may contribute towards either dispersing or directing the learners' attention (Robinson, 2011) during the process of task completion in video-based L2 listening.

Using a qualitative approach – mainly focus groups and interviews – the current study evaluated a number of tasks that were used for computer-based L2 listening when digital video is the mode of presentation. The participants, i.e. English as a foreign language (EFL) teachers and learners, were presented with a number of task designs to try and evaluate.

The findings revealed that some task designs are perceived to be less interactive and can disperse the learner's attentional resources during the process of task completion. They also shed light on the importance of improving EFL teachers' current practices of task design in computer-based L2 listening.

This paper has contributed to our growing understanding of interactivity in relation to video-based learning and its task designs.

This study aims to examine the effectiveness of bilingual learning strategies designed to support Chinese undergraduate business students facing significant learning challenges in an Australian university capstone curriculum delivered at their Chinese university. These challenges include the students’ difficulty understanding discipline-specific English terminology, using this terminology to discuss disciplinary concepts with their instructors and stress caused by an abnormally high study load.

In response to these challenges, the project team implemented a suite of bilingual strategies to reduce cognitive load and enhance learning, which included Chinese-English glossaries to build disciplinary-specific vocabularies; a bilingual teaching assistant to enable students to communicate in their language of choice; the use of WeChat to connect students to staff and to provide translanguaging opportunities; and bilateral managerial and academic support for strengthening the institutional cross-cultural relationship through staff exchange and language learning programs. A series of surveys were administered to measure the impact of these strategies on students’ learning, and WeChat logs were analysed to determine students’ linguistic preferences during discussions with staff and students.

The results of this project show strong support for each bilingual strategy, high academic performance amongst the student cohort, the positive contribution to learning and connection provided by social media technology, students’ language of choice preferences and chosen translanguaging styles and the important role of teaching staff in supporting international students’ intercultural learning and adaptation to a foreign university learning system.

This original evidence-based study helps to address the gap in bilingual education in Australian higher education demonstrating a successful strategy for dealing with language and discipline-specific challenges confronting EAL students.

This study aims to propose and test a model that examines the potential connections between two teacher situational variables (teacher immediacy and credibility) and three learner affective factors (motivation, attitudes and communication confidence) and to examine how such associations predict learners’ L2WTC (Foreign/second language willingness to communicate) in a language class via a comprehensive communication model to structurally verify the theoretically based associations among these variables.

In total, 214 females and 198 males took part in the study with age range between 19 and 38 years. Participants filled in a verified, translated Arabic version of the questionnaires using an online questionnaire. Data were gathered using questionnaires and were analyzed using descriptive statistics, confirmatory factor analysis, path analysis and sequential mediation analysis using bootstrapping methods to identify and verify direct and indirect paths in the model.

The initial L2 communication structural model showed acceptable goodness of model fit. Teacher credibility and immediacy behaviors only indirectly predicted L2WTC through the mediation of affective variables. Motivation and communication confidence mediated the relationship between credibility and L2WTC, while the association between immediacy and L2WTC was mediated by communication confidence.

The findings of this study have important pedagogical implications globally for professions related to communication instruction, especially with regard to teacher credibility behaviors and particularly for practitioners and beneficiaries in EFL contexts where learners are widely acknowledged for their unwillingness to communicate in foreign language classes.

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.

Given the continued globalization of industry, second language (L2) training is becoming more important. However, to assume that all managers who need L2 training are equally trainable or that all respond similarly to traditional L2 training techniques, can be costly errors of judgement. Attempts to address these issues using the results of a 1995 pilot NBA Spanish language training course for middle managers. Provides specific recommendations regarding these two important areas.

The use of three-dimensional virtual worlds (3DVWs) is increasingly becoming a common practice in language education to provide digital learning environments for second-language (L2) communicative classes. This study aimed to identify the key factors underlying communication in 3DVWs that can improve the communication skills of L2 learners.

To achieve this, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the identified factors affecting communication in 3DVWs. A self-reported questionnaire with 47 items on a five-point Likert scale was administered to 513 pre-service teachers, teachers and lecturers in the field of language education.

The results of the EFA revealed four factors that contribute to communication in 3DVWs, namely learner motivation, interaction pattern, language development and learner autonomy. CFA results provided support for the updated model, with statistically significant Chi-square results (χ² (df = 83) = 181.049, p < 0.001) indicating a good fit between the model and the data.

The findings suggest that the four EFA-derived parameters are valid and can assist instructional designers and L2 instructors in creating 3DVWs that enhance L2 learners' communication abilities. This study provides valuable insights for educators, instructional designers and researchers in the field of language education and technology-enhanced learning.

The present study focuses on the link between foreign language anxiety (FLA), self-perceived proficiency, and multilingualism in the under-explored English as a Foreign Language (EFL) context of Saudi Arabia. Ninety-six Arabic undergraduate college-level EFL students (56 males, 40 females) answered the Arabic version of the Foreign Language Classroom Anxiety Scale (FLCAS – Horwitz, Horwitz, & Cope, 1986). The analyses revealed that Saudi multilinguals suffered from low to moderate levels of FLA with female participants experiencing more anxiety than their male counterparts. Multiple regression analyses revealed that gender and self-perceived proficiency explained over a quarter of variance in FLA. Furthermore, the study did not find any role of experience abroad in predicting FLA.

Grounded in second-language acquisition (SLA) field, with a particular focus on the positive psychology (PP) theoretical perspective, this study examined the potential interplay between learning engagement (LE) and language learning strategies (LLSs), and their impact on language learning achievement of Saudi English as a foreign language (EFL) learners.

This quantitative study adopted a cross-sectional design using an online questionnaire distributed to 168 Saudi EFL college-level students in Saudi Arabia. Various statistical analyses (descriptive analyses, correlations and simple linear regression) were used.

The findings revealed that the most frequently LLSs used were metacognitive, followed by compensation, cognitive, affective, social and memory strategies. High levels of behavioral, followed by cognitive, emotional and agentic, engagement were reported. There was a significant and positive correlation between LLS and LE. LLS use and LE were significant predictors of language learning achievement.

The findings contribute to the domain of second language (L2) educational research and SLA field by emphasizing the importance of researching positive psychological factors such as engagement in relation to individual learners' learning strategies and styles to enhance learners' language learning achievement. A number of pedagogical implications for policymakers, educational stakeholders and foreign language teachers were provided.