Mobile English Research Articles

Interactive teaching design of mobile English based on deep learning

Interactive teaching design of mobile English based on deep learning

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.

A Study on English Writing Ability of Chinese Students Based on the Theory of Distributed Cognition

According to the Theory of Distributed Cognition, cognition is distributed in individuals, among individuals, in media and environment, etc. Researchers designed the project of Online English Writing and Learning, analyzed writing process of the non-English-major freshmen in China, and made a research on the practice of learning generating model of mobile English writing. It is found that the distributed cognition in media, individuals and community takes an important role in English writing, and it can reduce the learners’ cognitive load and improve their writing proficiency.

Syllabus Analysis of English for Hotel Receptionist Program with SKKNI in Kartu Prakerja by Mobile English

Hotel receptionists who work as frontline in the hospitality industry are supposed to be able to perform their duties no matter where the guests come from. In handling foreigners, English can be used as a medium to communicate and serve guests. To achieve a certain level of communication in English; the employee could take training programs and courses to upskill their language level in ESP. This study intends to analyze a syllabus of the ESP. By conducting this study, the researcher tries to find out the competencies being taught in the program and the suitability of the SKKNI as the standard reference. The method used in this study was qualitative research with a content analysis approach. The subject of this study was a syllabus developed by Mobile English as a learning institution that partners with the Kartu Prakerja Program. The instrument used to examine the study was a form of table. The study's results examined only five competencies taught in the program out of 14 competencies that should be acquired based on the SKKNI. The finding implies that future improvements in the syllabus and the program are needed to help the student be competent in their role as hotel receptionists

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

A study of English listening support teaching supported by mobile smart terminal APP

Abstract With the development of communication tools such as streaming media technology, education methods and concepts have changed accordingly. In this context, English listening teaching concepts and teaching methods have also changed. In this paper, by analyzing the advantages of the smart terminal APP, we propose a multiple-assisted English teaching model, construct a vector machine evaluation model based on the least squares method, and screen the most suitable mobile English listening assistance system for students through the meritocracy of particle swarm algorithm. Finally, the guidelines of teaching methods adapted to English listening assistance education for high school students were developed by combining support vector machines. The experiments show that the multi-source terminal teaching system in the context of mobile teaching transforms cell phones that interfere with classroom teaching into teaching assistants, improves the original listening teaching environment, breaks through the time and space limitations of the traditional listening teaching mode, and provides a reference for teaching English listening courses.

Developing English App Based Mobile Learning as the Innovation of English Learning Media

Abstract. This research aims to develop learning media as the innovation English app in enhancing the process of teaching-learning English in 4.0 Era. Through this app, it can be integrated by hybrid learning model that is prepared to deal with post-pandemic era challenge. This research was conducted based on the results of needs analysis of current learning that cannot be carried out fully face-to-face, as well as needs students and educators towards effective learning media. The objectives of this research are to elaborate the process of designing and developing learning media English App based M-Learning and examine the feasibility of that media in learning process of General English Courses in University of Billfath. The research design used is Research and Development (R&D) by applying the ADDIE model with five stages: analyze phase; design; development; implementation; evaluation. In further, the result of this research points out that in analyze phase found 71,3 % college students requires specific learning media-based M-learning that can be applied in hybrid learning model. While in the process of design and development English App as has been validated by experts that describe material validation 88,5 % and media validation 89% that can be said that the product is valid and deemed to be implemented. The stage of implementation is conducted seven meeting by hybrid learning. Furthermore, in evaluation stage indicates that the product has been feasibility to be applied in English learning due to the result obtained 80,6% (valid product). Hence, Mobile English App that is developed for college students in learning English has been succeed in fostering students ‘motivation and ability in General English Course.

Retracted: Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm.

[This retracts the article DOI: 10.1155/2022/7566896.].

Critical research trends of mobile technology-supported English language learning: A review of the top 100 highly cited articles.

Around the world, the number of English speakers and the significance of the English language are constantly increasing. Among various technology-supported instructional styles, Mobile Learning (M-Learning) has been recognized as a promising approach to enhance students’ competencies and skills in the English language. By examining previous literature, a number of reviews have been performed to investigate the role of M-learning in the English language. However, none of these studies has highlighted the trends, opportunities, and challenges identified in the most cited articles that focused solely on the English language. Therefore, to address these limitations, this study performed a review of the top 100 most cited articles, published between 2007 and 2020, indexed by the Web of Science, and addressing the English language only. The results revealed that most research in Mobile English Language Learning (M-ELL) followed an experimental design and employed a single mobile learning implementation. Additionally, the current study identified a number of research areas that require additional research attention. For example, further research is needed among students learning from home, more qualitative research is needed, and additional research is required to improve students’ higher-order thinking skills. The outcomes of this study provide a reference to researchers and educators who intend to use mobile technologies in the area of language education, especially in the context of the English language.

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.

Motivation, Learning Strategies, and Outcomes in Mobile English Language Learning

Motivation, Learning Strategies, and Outcomes in Mobile English Language Learning

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.

Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm.

With the rapid development of information technology, today's talent training mode is no longer limited to traditional school education. At the same time, with the maturity of portable mobile devices, a new learning method—mobile learning has been born. In this paper, the narrative of data is taken into account in the way of user collaborative filtering recommendation. For prefilling the matrix, the project confidence level also needs to be considered during the whole process. The project confidence level is measured by information entropy model. In the process of correction, it combines with traditional cosine similarity, calculates the user similarity matrix, can budget equalization, and expands the original matrix. After filling the matrix, the user uses the method of similarity calculation, using Pearson similarity and combining with the Euclidean distance correction method. When comparing the particular result prediction with the actual prediction following the completion of the similarity matrix data, all results point to a considerable reduction in MAE and RMSE. The user does not assess the item score to forecast. This demonstrates how this technique may enhance the reliability and consistency of the mobile English system platform.

New Qualitative Perspective in Human-Computer Interaction: Designing Mobile English for STEM.

The emerging field of human–computer interaction (HCI) opens up more opportunities for technology integration in language learning. Technology creates more workforces in the Science, Technology, Engineering, Mathematics (STEM) field, yet the number of STEM pursuers is declining due to poor command of the English language. There is a gap in English vocabulary for STEM, which needs a novel solution from the perspective of HCI. This study aims to explore the needs of STEM learners in creating an English language competency mobile module. The methodology used is through a qualitative study. Seven STEM learners, 17 years old, participated in semi-structured interviews. The results from the interview are divided into four main themes: (1) the importance of learning English, (2) problems of learners, (3) strategies of English language learning, and (4) learners' readiness in using a mobile app. This study is significant for mobile app designers, English language teachers, and course designers as the input will provide an overview of the STEM learners' needs in the English language. Plus, designing a mobile app from the learners' perspectives gives more effectiveness to HCI, rendering success to second language acquisition. Future work can design and develop a mobile app to enhance STEM learners' English language competency based on the learners' perspectives.

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%.

Remaking “Englishness” and Place: John Stapylton Grey Pemberton's Nineteenth-century Accounts of the Indian Rebellion Sites at Kanpur and Lucknow

This article focusses on a trip made by John Stapylton Grey Pemberton in 1887 to two major battle sites from the Indian Rebellion of 1857, the Memorial Well Gardens, Kanpur, and the Residency ruins, Lucknow. Both sites, despite being present in a foreign country, were invented and transformed after the rebellion in acts of national remembrance as places of ‘Englishness’. The selection of Pemberton's accounts are intended to explore how colonial spaces entered the discourse on ‘Englishness’, and how English colonists attempted to manifest their cultural identities and discipline the identities of their subordinates. By deciding to multiply its locations of identity beyond its own shores, the British Empire ensured that England would lose sovereign command of its ‘own’ spaces of identity. Kanpur and Lucknow were sites where England's narrative of belonging was de-stabilized and recreated, altered further by colonial subjects like Pemberton who came into contact with them. The idea of an autonomous English colonial space, separate from that of the colonised, needs to be replaced with a more fluid and mobile English subject.

Intelligent Interactive English Teaching Discrete Data Modeling and Simulation

This study attempts to explore the discrete data modeling and simulation of smart interactive English teaching based on mobile APP, expecting to explore the effective teaching strategies for deep integration of mobile APP technology and smart interactive English teaching, and to conduct discrete data modeling and simulation of English teaching. In this study, we propose a discrete data modeling algorithm for smart interactive English teaching based on discrete data points and normal vectors using B splines. By adding normal vector constraints, this method can maintain good fitting results even when the data points are discrete. The method consists of four parts, namely, discrete point parameterization, B-spline dominant point selection, node vector determination, and least-squares fitting, including both data point error and normal vector error. The mobile APP-based initial intelligent interactive English teaching should adhere to the four principles of autonomy of English course learning, adhere to teacher dominance in the language classroom, grasp the humanistic nature of English course content, and focus on the practical nature of English course implementation. The interactive learning environment based on mobile APP can promote students’ in-depth understanding of knowledge. The higher the student’s interest in learning, the deeper the degree of understanding; the more diverse the learning methods, the more it can promote students’ in-depth understanding of knowledge; the stronger the degree of interaction, the higher the level of understanding. The discrete data modeling of smart interactive English teaching proposed in this study provides a theoretical reference for the design and production of interactive learning resources for English courses, according to which the interactive learning resources for English courses are produced to make up for the lack of English course resources.