Mobile English Learning Research Articles

College English Mobile Learning Patterns Based on Multi-Modal Learning From “Internet +” Perspective

Under the background of Internet Plus, the rapid development of the mobile Internet technology has led to the widespread use of the PCs and smartphones, bringing out new chances for college English mobile learning. More importantly, with the trend of globalization, the complex situation has set higher demands for this generation. In order to promote college students’ English learning effectiveness and enhance comprehensive English skills, this paper explores possible mobile learning patterns based on multi-modal learning that college students can apply in English learning, according to the theories of the Multi-modal Discourse Analysis and mobile learning. Included in the study are the employment of questionnaires and interviews. The results show that teachers’ construction of multi-modal teaching and students’ after-class multi-modal learning play a significant role in improving college students’ English proficiency. The research, therefore, will provide some possible insight into cultivating college students’ comprehensive English skills to adapt to the changing society.

Behavioral Intentions of Low-Achieving Students to Use Mobile English Learning: Integrating Self-Determination Theory, Theory of Planned Behavior, and Technology Acceptance Model Approaches

This study aims to examine the factors influencing students’ behavioral intentions toward using Mobile English Learning (MEL) by integrating three key theoretical frameworks: the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Theory of Planned Behavior (TPB). SmartPLS and SPSS were used to analyze data from a survey involving 499 students with low-achieving academic records from six Taiwanese universities. The results show that perceived autonomy, perceived relatedness, and perceived ease of use predict perceived usefulness. Furthermore, attitude, subjective norm, and perceived behavioral control predict perceived ease of use. Both perceived usefulness and perceived ease of use significantly and positively influence behavioral intention. These results provide a theoretical basis for promoting the intention of low-achieving students to use MEL.

Surveying on English Mobile Learning Among University Students: Current State and Influencing Factors

Surveying on English Mobile Learning Among University Students: Current State and Influencing Factors

Retracted: Design of English Mobile Learning Platform Based on GSM-R Wireless Network Communication System

Retracted: Design of English Mobile Learning Platform Based on GSM-R Wireless Network Communication System

Research on the Innovation of University English Teaching Mode Assisted by Mobile Technology Combined with SAMR Model

Abstract With the burgeoning advancements in mobile technology, mobile learning has garnered significant interest across various sectors, emphasizing its crucial role in enhancing educational frameworks and nurturing students’ core competencies. This study uses the UTAUT model to create a research framework for the factors that affect teaching college English using mobile technology. Taking first-year students to junior students of a university as an example, the study collects relevant data through questionnaire surveys. It carries out correlation analysis, difference test, and regression analysis to explore the factors influencing college students’ English mobile learning. Then, combined with the SAMR model, the English teaching model of online integration and teacher-student synergy is proposed. The design of teaching experiments is used to study the effect of this paper’s teaching model. The analysis shows that community influence, performance expectations, effort expectations, content and resources, and perceived self-efficacy all have a positive effect on mobile technology use in English teaching (p < 0.001), with the effects of perceived self-efficacy (0.363) and performance expectations (0.331) being particularly significant. The receptive vocabulary, output vocabulary, and comprehensive test of the students in the experimental class improved by 12.34%, 20.65%, and 15.84%, respectively, and the application of mobile technology under the SAMR model has a significant contribution to the improvement of college students’ English proficiency.

A Mobile English Learning Platform Based on Data Mining and Personalized Recommendations

Abstract As mobile technology advances, mobile English learning platforms have increasingly become the predominant mode of language acquisition. However, traditional platforms often suffer from inefficiency due to a lack of targeted and personalized recommendations. This paper explores the enhancement of mobile English learning platforms through the application of customized recommendation algorithms and data mining techniques. We have developed a system architecture composed of a user layer, a business layer, and a data layer, employing Spark and Support Vector Machine (SVM) for parallel data processing. Our approach integrates a collaborative filtering algorithm and a hybrid recommendation model, utilizing user information alongside the dual-tower model Deep Structured Semantic Model (DSSM) and the Deep Interest Network (DIN). Experimental results demonstrate that our recommendation model significantly surpasses traditional models in precision, recall, and F1 scores, with the F1 score improving by approximately 0.1 to 0.4. Furthermore, learners using this platform showed superior outcomes in homework completion, test scores, learning quality, and sustained learning enthusiasm compared to a control group, with an overall improvement of 15.48 points in English proficiency scores. In summary, this study validates the effectiveness of a mobile English learning platform that synergizes data mining with personalized recommendation algorithms to enhance both learning efficiency and user satisfaction.

Retracted: Research on Mobile English Learning System Based on iOS

Retracted: Research on Mobile English Learning System Based on iOS

Retracted: An English Mobile Learning Platform Embedded in GSM-R Wireless Network Communication

Retracted: An English Mobile Learning Platform Embedded in GSM-R Wireless Network Communication

A study on factors influencing Chinese undergraduate EFL learners' self-directed use of mobile English learning resources.

This study aimed to examine the factors that potentially impact the self-directed use of mobile English learning resources (MELR). The participants were 206 Chinese undergraduate EFL learners at Yangzhou University in Mainland China. Applying and modifying the Unified Theory of Acceptance and Use of Technology (UTAUT), this study involved six constructs, including students' performance expectancy, effort expectancy, social influence, facilitating conditions, perceived playfulness, and behavioral intention to use MELR. The structural equation modeling (SEM) technique was adopted to analyze the data collected from the questionnaire. The findings showed that facilitating conditions acted as the most significant predictor of behavioral intention to adopt MELR, followed by effort expectancy, perceived playfulness, and performance expectancy. However, social influence did not have significant effects on students' use of MELR. Pedagogical implications for teachers and students were also presented in the end.

Retracted: Construction of College English Mobile Learning Model Based on Distributed Terminal

Retracted: Construction of College English Mobile Learning Model Based on Distributed Terminal

EFL Tertiary Learners' Perceptions on Ease of Use of Portable Devices in English Mobile Learning at a University in Ho Chi Minh City, Vietnam

As the remarkable breakthrough of the age of revolution 4.0, mobile learning (M-learning) appears among the sharp development of modern technology. Their clever features have been discovered to have accompanied the exponential growth of mobile devices to enhance students' learning whenever and wherever. Mobile-assisted language learning (MALL) has so been considered to benefit individuals' English proficiency. Students' attitudes on utilizing mobile devices for learning while being connected to a wireless network need to be carried out, even though learning through mobile devices is expected to apply appropriately to teaching and learning. Through a survey of 207 university EFL students using a questionnaire with 13 English-Vietnamese statements within 20 minutes, this article aims to exhibit EFL learners' behaviors and acceptance of the use of mobile devices in English learning at a university in Vietnam. The survey ran within two weeks in the first semester of the 2019–2020 school year at a University in Ho Chi Minh City, Vietnam. A quantitative approach was used to gather and analyze data from the questionnaire under SPSS version 20. Research findings demonstrated that learners' opinions regarding adopting portable technology in English learning are favorable. The learner supports this virtual learning thanks to mobile gadgets' smart functions offering convenient learning without demanding users' efforts to manipulate. Neither my current university firmly welcomes mobile learning applications to serve flexible education and training in the future.

Evaluation System of Mobile English Learning Platform by Using Deep Learning Algorithm

At present, China’s economic development continues to progress and foreign exchanges are also increasingly frequent. Learning to master the world’s universal pronunciation, English, has become a more important link. However, people’s living habits make it difficult to carry heavy desktop devices for a long time, so mobile English learning platform meets the development needs of people’s English teaching. Based on the existing English mobile learning platform, this paper puts forward the concept of integrating artificial intelligence and deep learning technology into it. Through deep learning, the learning status and learning situation of students in the process of English learning are extracted, so as to analyze the needs and learning interests of each student in English learning and then push corresponding English materials to each student, which improves the efficiency of English learning. In addition, deep learning can also model the data of students’ behaviors and build a language vector feature extraction mechanism and translation quality evaluation model, so as to carry out certain intelligent auxiliary correction and correction on students’ English grammatical expressions and spoken English. The research in this paper has achieved good results through practice. The practice results show that the integration of deep learning into the existing mobile English teaching platform can optimize the functions of the existing platform, provide more ideas for the development of online English learning, and has good theoretical and practical value.

A Case Study of College Students’ EFL Learning Experiences on Mobile Phone Applications

Mobile English learning (MEL) has become increasingly popular for its diverse advantages especially since the global COVID-19 pandemic. While a lot of studies have confirmed effectiveness of the mobile approach to learning English in a pedagogical context, there is very limited research that focuses on how students’ MEL experiences vary depending on their individual habits and behaviors out of class. This study is aimed to explore college students’ experiences of learning English as a foreign language (EFL) on mobile phone terminals. To this end, the study was conducted with a sample of 359 undergraduate students from 5 different grades by looking into their time spent after class in learning English on mobile phone applications (MPA), their practice preferences in MEL, the number of MPA used and their views on the helpfulness of EFL learning on MPA. The quantitative results derived provide a relatively comprehensive knowledge of the present situation of MEL in a higher education context. Statistically significant findings of the study are hopefully to offer insights for both policymakers and instructors in higher institutions to understand and promote MEL from more scientific and multi-dimensional perspectives.

Examining the moderating role of technostress and compatibility in EFL Learners' mobile learning adoption: A perspective from the theory of planned behaviour.

Mobile English learning has multiple advantages and brings enormous benefits to EFL learners. However, not everyone adopts it, and the determinants of learners’ adoption intention have not yet been investigated fully. This study aimed to better understand learners’ adoption by employing the theory of planned behaviour (TPB) in conjunction with the influences of technostress and compatibility. Based on existing literature, a research model was proposed and verified with a sample of 409 undergraduates from a Chinese university. The results indicated that in the context of mobile English learning: (a) Individuals with higher adoption intention are more likely to engage in mobile English learning with higher frequency (b = 0.473, P < 0.001) and longer duration (b = 0.330, P < 0.001); (b) Individuals’ attitude toward mobile English learning (b = 0.171, P < 0.05), perceived behavioural control (b = 0.221, P < 0.001), subjective norms (b = 0.237, P < 0.05), and compatibility (b = 0.443, P < 0.001) are significantly positively associated with their adoption intention; (c) Compatibility is the strongest predictor of adoption intention (b = 0.443, P < 0.001) and negatively moderates the effect of subjective norms on adoption intention (b = –0.103, P < 0.005); (d) The influence of technostress on the adoption intention of mobile English learning is not significant (b = –0.041, P > 0.05). Practical implications related to mobile English learning were discussed.

Application of an Internet of Things Oriented Network Education Platform in English Language Teaching

China’s information technology development is rapid; the organic combination of science and technology and education has promoted the reform of the education system; the education platform supplemented by mobile devices provides students with a new learning mode; student learning is no longer limited by the traditional education system; and learners can use mobile terminals for online learning, self-control learning steps, arrange learning time, and be able to combine their own deficiencies to strengthen learning content. Therefore, this paper discusses the design of English education teaching system in the Internet of Things technology environment, in order to provide students with a good learning environment and improve students’ English integration strength. This paper analyzes the research status of mobile learning at home and abroad, expounds the construction principles of mobile English learning system, and designs and studies the overall structure and functional modules of the system. The application of the system can replace the traditional English learning method and can enable students to improve their English ability through mobile devices. In view of the problems of poor teaching effect and high energy consumption in the traditional Internet of Things education platform, this paper puts forward a teaching effect evaluation method of Internet of Things education platform based on long-term memory network. By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. Finally, through the study of the model, the teaching effect of the Internet of Things education platform is evaluated. The experimental results show that when the method is used to evaluate the teaching effect, the operating energy consumption accounts for 84% of the total energy consumption of the system, which proves the effectiveness of the method.

Evaluating the Effect of EFL College Students’ Intention To Utilize Mobile English Vocabulary in The Learning Process: A TAM Framework

In today's world, it is no longer a surprise that (smart) mobile devices have become the most helpful technological devices to be used for various purposes, one of which is in English education. Therefore, studies on improving language learning, especially vocabulary for learners using mobile device technology, have become commonplace in today's digital era. However, to achieve maximum use, there needs to be an intention from students to accept the English vocabulary technology in the learning process. Therefore, the purpose of this study was to evaluate the integration capability of the Technology Acceptance Model (TAM) to predict and explain EFL college students' intention to utilize mobile English vocabulary learning. This research involves TAM because TAM has been widely applied to study information technology because of its effectiveness in assessing the level of user acceptance. Through a quantitative method using a cross-sectional survey approach involving 456 respondents, this study ultimately found that, after being analyzed using the PLS-SEM analysis technique, the constructs in TAM, including perceived ease of use (β = 0.302, T-Value = 6.587, and P-value = 0.000) and usefulness (β = 0.359, T-Value = 7.501, dan P-value = 0.000), had a positive and significant effect on EFL college students' intention to utilize mobile English vocabulary. Perceived usefulness shows the most dominant effect. However, this study has limitations that need to be considered. And, of course, caution in generalizing is necessary.

Evaluating the Design and Teaching Effect of College Mobile English Learning Using Deep Learning Technique

Mobile learning has emerged as a popular model for the teaching and learning process, which can also be described as a modernised version of the traditional classroom learning process. As modern education has been implemented, the educational level of both students and teachers has improved. This advancement is made possible by increased knowledge of how to use mobile technology with the Internet. In this study, a multiprocessor learning-based CNN algorithm is proposed to improve students’ English language speaking fluency. Additionally, the students and the teachers are considered available in the remote area, and the communication between them is done through an interactive system. The model is designed to focus on a teacher-centric perspective, in which the teacher has to make use of multiple resources or applications to perform an easier understanding of the course for the students. Hence, an intelligent system is designed to make the learning processes successful. The results show that the proposed algorithm works well in improving the teaching efficiency of mobile English learning. It also revealed that the accuracy level of the learning system used by college and university students has increased by 10%.

A Study on College English Mobile Learning Teaching Strategies Based on Smart-phone Applications

With the development of information technology, Micro-course, Wisdom Class, Massive Open Online Courses, and so on have emerged. Smart-phones, as one of the modern mobile learning devices, come to learners’ rescue to get access to those brand-new learning styles. Students can more easily achieve autonomous learning anytime, anywhere with smart-phone apps. In the teaching practice and experience, there still exists some problems in the traditional process of English teaching in college, such as single English teaching model, rigid English learning methods and “dumb” English. This paper proposes some teaching methods that combine smart-phone apps with mobile English learning. These strategies can enhance students’ self-control and self-learning ability and improve the efficiency of English teaching.

Construction of College English Mobile Learning Model Based on Distributed Terminal

The quality of English teaching cannot be separated from the students’s accumulation and mastery of vocabulary, and sufficient vocabulary can ensure that students feel easy and comfortable in the language application. However, there are still some problems in teaching English vocabulary in colleges and universities, such as single presentation method, lack of real context, and other difficulties. With the rapid development of mobile Internet, the share of smart mobile devices among college students has increased, which in turn has laid the foundation for the development of new learning modes. The mobile terminal-mediated learning mode in English classroom teaching has become a hot topic of research nowadays, of which English vocabulary teaching has become a typical example. This paper is designed as a system model of edge computing, in which the user terminal device can connect wirelessly with each MECS and offload the tasks to be processed on the MECS, which helps the user terminal device to process and return the results to the user terminal device. The research in this paper also provides a basis for students’ independent and lifelong learning of English words and for schools’ information technology to serve English vocabulary teaching and learning.

A comprehensive analysis of the 21st century's research trends in English Mobile Learning: a bibliographic review of the literature

PurposeThis study examines the importance of English Mobile Learning research as a foundation for lifelong and sustainable education from different points of view, including those of technology innovation experts, psychologists and educators. It aims to explore the current status and relevant research trends through the application of bibliometric mapping and bibliometric analysis.Design/methodology/approachFor this study, all Web of Science records (in total 5,343) from 2000 to 2020 in the field of English Mobile Learning were analyzed using the VOSviewer and CiteSpace software tools. The WoS built-in functions, including “Refine” and “Analyze,” were employed to perform the bibliometric analysis. The study further analyzed a sample of the five most-cited articles to identify the previous studies with the highest quality or impact.FindingsThe results showed that research in English Mobile Learning is growing quickly and steadily with a noticeable emphasis on various device-based technologies and applications. The study also discusses the key implications for research institutions, education policymakers and academicians, and identifies the most prominent avenues for future research on English Mobile Learning. Moreover, the results shared in this review highlight the most important and emerging areas of research in the field.Originality/valueThis article is the most recent bibliographic review of literature that particularly addresses the English Mobile Learning research during the past two decades.