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Based on the premise that computers have now become cultural and cognitive artifacts with which and not from which learners interact on a daily basis, this chapter focuses on best practices in preparing and engaging digital natives to become tomorrow’s leaders of a global knowledge economy that is increasingly dependent on electronic modes of communications. Using a study based on online tools in a writing course taught at the University of Victoria (Canada), we take a qualitative interpretative stance to explain the opportunities and challenges of learning and teaching in such environments. We comment on such aspects as the need to properly address learner’s functional skills (or lack off), the various tools that can be used to engage and motivate learners, and the need to go beyond methods based on delivery in order to better focus on the development of multiliteracies, in particular critical literacy and functional literacy. Our argument, grounded in cognitive and sociocultural theories of learning, favors an interdisciplinary approach while focusing on disciplines that are typically housed in the humanities, in particular second language academic programs. Our discussions and conclusions move from these case studies to a more general reflection on the extent to which electronic environments are reshaping higher education.

This study describes a telecollaborative project in an upper-level French language course at an American university from the students’ perspectives. The project involved synchronous computer-mediated communications via the online videoconference platform Skype between US-based French language learners and French native speakers in France. In order to increase the participants’ interest and engagement in the virtual exchanges, the telecollaboration employed critical approaches in the task design. In this telecollaboration, students were asked not only to take part in an intercultural exchange with their partners on potentially sensitive topics that included freedom (e.g., freedom of speech, religious liberty), globalization (e.g., child labor), and immigration (e.g., racism, xenophobia), but also to engage in a short debate on these topics. An online anonymous survey was used to solicit their reactions and attitudes toward this critical approach, as well as toward the technology-enhanced learning activity as a whole. The qualitative analysis of the students’ responses showed that the telecollaboration project was generally well received, despite the inclusion of sensitive topics. Most students indicated that they felt most challenged by and most apprehensive about the topic of immigration, which was attributed to the concurrent complex socio-political situation at the time they participated in the telecollaboration project. High levels of anxiety were also reported from the youngest participants, those who majored or minored in other disciplines than French, and non-degree students. This exploratory study calls for more data and an in-depth analysis of the student’s discourse, especially with respect to potential differences in pragmatic strategies used for addressing sensitive versus less sensitive topics in the target language during virtual exchanges with native speakers in that target language.

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.

In the field of second language acquisition, much attention has been paid to which variables (such as age, race, social class, ethnicity or gender) have influence on language learning, or to how such variables may affect language acquisition. The intent of this study was to examine gender at an intersection with ethnicity by exploring it within an ESL experience. Ethnography as method helped such an exploration. The past twenty-five years or so have presented educators with a wealth of research on what happens to girls in schools, though female ESL students may not be benefiting from this same research (Cochran, 1996; Sunderland, 1994, 1995, 1998; Vandrick, 1999a, b; Willett, 1996; Yepez, 1994). As a result, there is a compelling need to bring feminist pedagogical research to ESL research. In this ethnography the amount of talk in an ESL classroom was measured and discussed, settling largely on a lack of linguistic space of girls in this language learning context. The concept of ‘linguistic space’ was first used by Mahony (1985) when referring to conversational participation in a British classroom. This study borrows her use of the term as a way to explore the language production in a Canadian ESL classroom.

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.

This chapter will focus on how inclusive pedagogic practices can be played out in primary and secondary classrooms where the goal is using languages other than the learners’ home language as both the medium and content of learning (i.e. learning to use language and using languages to learn). This requires an approach which is inclusive, flexible and relates to any context – both languages and subject classrooms. The focus will be on how using an integrated approach to the curriculum, in which languages are used as a tool for learning, has the potential to be motivating and accessible to very diverse learners.

The chapter includes two lessons – the primary lesson plan will expand how simple language can be used to develop and enjoy painting and art with young students and the secondary lesson plan will focus on how a visual approach to thematic or cross-disciplinary work, such as natural disasters, can supplement and support deeper understanding of other areas of the curriculum as well as building confidence in communicating in an alternative language.

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.