College English Research Articles

Investigation of Cultural Identity of Students Who Study Japanese Language at British Universities Between 2012-2023

In Japanese language classrooms, students have become more globalised in terms of the students’ educational and life experience background. Teaching pedagogies which have been used before globalisation may not be appropriate and effective to students after globalisation. The aim of this study is to investigate any cultural identity characteristics of students who studied Japanese at two British universities in the South of England between 2012 and 2023. The significance of this study is to investigate how students’ individualist or collectivist cultural background may affect students’ cultural identity. Participants were a total of 22 students who studied Japanese language through an Institution-Wide Language Programme (IWLP). The majority of these students have studied Japanese for a minimum of one year up to three years. The data was collected between 2012/13 and 2022/23 academic year from two British universities. The research design to investigate students’ cultural identity characteristics was a mixed method, using variables such as hometown, languages they speak, their values and their first name. Students’ nationality, where they were born and where they were educated were also taken into consideration to analyse the data. The key finding of students’ characteristic was that students’ identification was closely related to their sense of belonging in the results of students’ hometown, languages they speak, their values and their first name, which is in line with other previous research results in the literature. Other findings included that some of the students found it difficult to determine their hometown, that the majority of students were multilingual who have experience in several language learning, that students’ values were often influenced by their parents’ individualist or collectivist cultural values, and that students’ first names often showed the students’ identification and sense of belonging. Taking into consideration of individualist or collectivist culture, recommendations discuss on how the language teaching practitioners may be able to support the sense of belonging of the current students who have diverse educational and life background.

OBE 理念下大学英语学生自主学习能力培养策略研究

Under the OBE (Outcome-Based Education) concept, cultivating students’ autonomous learning ability is an important task in College English courses, requiring the joint efforts of teachers and students. Only by creating a positive learning environment, guiding students to set learning goals and plans, inspiring students’ interest in learning and self-motivation, and establishing effective feedback mechanisms, can students’ autonomous learning ability be effectively cultivated. This enhances their English proficiency and lays a solid foundation for their future development. This paper proposes strategies for cultivating students’ autonomous learning ability from the OBE concept perspective and in conjunction with the content of College English courses, aiming for students to continuously improve their comprehensive English learning ability through autonomous learning.

高影响力教学在大学英语课堂中的应用

With the acceleration of globalization and the increasing demand for cross-cultural communication, the teaching methods of college English classes need to be constantly innovative. High-impact teaching is a studentcentered teaching method, which aims to stimulate students’ learning enthusiasm and initiative through diversified teaching activities and teaching resources, and cultivate students’ comprehensive quality and ability. This paper aims to explore the application of high-impact teaching in college English classes and puts forward some specific application strategies and suggestions through analyzing the theoretical basis and characteristics of high-impact teaching and combining with the actual situation of college English classrooms. This paper holds that high-impact teaching can effectively improve students’ learning motivation and interest, promote students’ active learning and cooperative learning, improve students’ comprehensive English application ability and cross-cultural communication ability, and also improve teaching quality and effect. This paper also looks forward to the future research direction, hoping to provide more in-depth theoretical and practical support for the application of highimpact teaching in college English classes.

Improving academic writing skill: Difficulties encountered by undergraduates of English studies in Bangladesh

Bangladeshi students of English programs encounter some barriers in their attempt to develop academic writing skill at undergraduate level. The majority of the students who study in English programs in Bangladesh receive their primary and secondary schooling in the instruction medium of Bangla, while a few percent in English medium and English version instructions. Testing and assessment in English studies predominantly focuses on academic writing skill and many students cut a sorry figure in formative and summative assessments in the very first semester due to poor writing competence. This study examined two issues: the difficulties that Bangladeshi undergraduates of English studies confront while developing academic writing skill, and the ways to develop L2 English writing skill. Participants of this study were 6 students studying English in different Bangladeshi universities and 3 English teachers who were teaching at tertiary level. Data collection tools were composition writing samples on a selected topic for undergraduates and a semi-structured interview schedule for teachers. Results indicated that poor grammatical competence and brainstorming skill, insufficient vocabulary, inadequate knowledge of mechanics and writing style, and mother tongue interference were the major barriers that Bangladeshi undergraduates of English studies frequently faced while writing in English.

THE USE OF INFORMATION COMMUNICATION AND TECHNOLOGY (ICT) IN ENGLISH LANGUAGE LEARNING: A CASE STUDY AT ONE PUBLIC UNIVERSITY IN JAMBI

This research is aimed to describe students’ perception toward the use of Information Communication and Technology (ICT) in language learning. The objective of the study is to know students’ perception toward the use of ICT in language learning and to know the problem that they encountered during the use of ICT. The design of this research was survey method. The subject of this research was sixth semester students’ of English department Jambi University. A questionnaire was main instrument to gather the data. The questionnaire consists of items regarding the educational resources of respondents, their reasons for using ICT, the use of ICT in learning process and the problems that they encountered. The result of the study showed that ICT is useful for students. Using ICT can help students easier in accessing information they need which can bring positive effect for students, but this should supported by sufficient ICT equipment and ability to determine information which they search for in order to give better performance since the number one problem faced by students come from two numbers which are, few staff using ICT and information from ICT always change. It is recommended for lecturer to involve ICT services during the teaching and learning process more often and giving guidance to students to determine the appropriate material for their needs. Keyword: Information communication and technology, English language learning

Grammatical Competence of UM Digos College English Language Learners

The primary objective of teaching grammar is to equip students with an understanding of the structural aspects of language, enabling them to effectively comprehend, communicate, and articulate themselves through listening, speaking, reading, and writing. This study aims to determine the impact on grammatical proficiency by scrutinizing the different parts of grammar, such as nouns, pronouns, verbs, adjectives, adverbs, prepositions, conjunctions, and interjections, in the essays submitted by 39 third-year BSED English students at UMDC. Moreover, the tool employed is the Grammarly application, which incorporates Grammarly functionality. Based on the findings, it is evident that language learners had experienced significant challenges with a diverse set of grammatical structures, such as nouns, verbs, and adjectives, which are areas where students frequently make mistakes. Language educators are thus faced with employing inventive and pioneering methods to teach grammar to accomplish this objective effectively. Regardless of the activities assigned, the teacher needs to offer students the chance to generate grammatical structures using accurate examples of sentences that are syntactically and semantically acceptable and include suitable and relevant terminology.

College English Teaching Classroom Based on Fuzzy Mathematical Model

The use of English in daily communication is becoming more and more important. The degree of mastery of English is largely related to the quality of college English teaching. College English Teaching (CET) has always been the focus of college classroom teaching. The quality of CET directly affects the ability of college students to master English. The evaluation of College English Classroom Teaching (CECT) can reflect the quality of CECT, but the traditional CECT evaluation mainly adopts the average method to analyze, which leads to insufficient understanding of the teaching effect by teachers and low English learning efficiency of students. CET has a fuzzy boundary, and the fuzzy mathematical model can be used to analyze the evaluation indexes that affect the effect of CET and the relative weights of each index, so as to conduct a comprehensive evaluation of CET. A comparative analysis is made between the CET classroom based on fuzzy mathematical model and the traditional mean CET classroom. The experimental results show that the teaching efficiency of practical CET under the two CET evaluation methods is 63% and 75.7%, respectively, while the efficiency of professional CET is about 65.7% and 82.7%, respectively. It can be shown that analyzing CET through Fuzzy Comprehensive Evaluation (FCE) can improve the teaching efficiency of college English classrooms and promote the mastery of English.

Evaluating the Learning Effectiveness of A Blended Teaching Model of College English Using A Decision Tree Algorithm

The learning effectiveness of a blended teaching model of college English, utilizing a decision tree algorithm, demonstrates promising outcomes. This model combines traditional classroom instruction with online learning components, allowing for personalized and interactive learning experiences. By employing decision tree algorithms to analyze student data and performance metrics, instructors can tailor instructional strategies to individual learning styles and needs. This data-driven approach facilitates targeted interventions, adaptive feedback, and optimized course materials, ultimately enhancing students' English language proficiency and academic success. The blended teaching model harnesses the power of technology and data analytics to create dynamic and effective learning environments in college English education. \College English education. The HGBT-DT model combines genetic algorithms, blended teaching methods, and decision tree algorithms to tailor instructional strategies to individual student needs. Through a comprehensive study involving 100 students, we demonstrate the efficacy of the HGBT-DT model in enhancing language proficiency and critical thinking skills. Results indicate a significant improvement in student performance, with an average post-test score increase of 15 points compared to pre-test scores. Additionally, the HGBT-DT model achieves an optimization score of 0.90, indicating a high level of effectiveness in optimizing teaching strategies. Furthermore, decision tree analysis reveals personalized teaching recommendations, leading to improved student outcomes across diverse learner profiles. This study highlights the potential of the HGBT-DT model to revolutionize teaching practices and promote personalized learning experiences in College English education.

Values and Paths of Ideological and Political Education in College English Teaching in Applied Universities

As the state promotes teaching reform with the goal of ideological and political education, the teaching of various disciplines in colleges and universities is facing new challenges and novel research topics. In applied colleges and universities, as a compulsory course of general education, the ideological and political education elements have not been deeply explored, and the function of ideological and political education that should be undertaken by English teaching has not been fully realized. This paper analyzes the value of ideological and political education in college English in applied colleges and universities and discusses the effective path of ideological and political education in college English in applied colleges and universities from the four levels of teachers, teaching materials, teaching methods, and curriculum.

Blended Teaching Mode Model of College English Based on Collaborative Filtering Recommendation Algorithm

The incorporation of collaborative filtering recommendation algorithms into blended teaching approaches for college English marks a big step forward in language education. This study investigates the theoretical underpinnings, practical ramifications, and pedagogical issues for adding collaborative filtering algorithms into blended learning environments. Collaborative filtering algorithms produce individualized learning resource recommendations based on user interactions and preferences, increasing student engagement and motivation. However, issues like as data privacy, algorithmic biases, and user approval must be addressed to ensure that collaborative filtering is implemented effectively in the classroom. This study explains the potential of collaborative filtering recommendation algorithms to improve language learning results in college English courses by doing a thorough literature review. It also provides ideas for educators and policymakers on how to handle the complexity of incorporating collaborative filtering recommendation algorithms into blended learning models. Finally, this paper adds to the promotion of novel pedagogical approaches and the improvement of language learning experiences among college students. As a result, the enhanced collaborative filtering recommendation system had a slightly greater recall rate and precision than the pre-optimized method.

Corrigendum to “Exploring Entrepreneurship Education Effectiveness at British Universities – An Application of the World Café Method”

Corrigendum to “Exploring Entrepreneurship Education Effectiveness at British Universities – An Application of the World Café Method”

A Blended Teaching Model of College English Based on Deep Learning

Blending traditional face-to-face instruction with online learning components, this model aims to enhance student engagement and improve learning outcomes. This paper introduces a novel blended teaching model for college English courses, integrating Conditional Random Field (CRF) techniques with deep learning frameworks. Blended learning, combining traditional face-to-face instruction with online learning components, has gained traction in higher education for its potential to enhance student engagement and learning outcomes. CRF blended teaching integrates the principles of conditional random fields, a powerful probabilistic graphical model, with traditional teaching methodologies to enhance learning outcomes. Unlike conventional teaching approaches that often rely on linear or static instructional methods, CRF blended teaching embraces a more dynamic and adaptive framework. By leveraging CRF, which models the dependencies between different instructional variables, educators can design personalized learning experiences tailored to individual student needs and learning styles. This approach enables the seamless integration of diverse instructional modalities, including face-to-face instruction, online resources, and interactive learning activities. The proposed model CRF algorithms to effectively capture the sequential dependencies inherent in language learning tasks, such as sentence parsing and part-of-speech tagging. By integrating deep learning architectures, including recurrent neural networks (RNNs) and transformers, model with large-scale linguistic corpora to improve accuracy and generalization. The model achieves a 15% increase in students' English proficiency scores and a 25% improvement in comprehension compared to traditional teaching methods, underscoring its effectiveness in enhancing learning outcomes.

Using Speech Recognition Algorithms to Improve Listening Training in College English Listening Instruction

English listening proficiency plays a crucial role in language learning and communication. Speech recognition algorithms can significantly enhance listening training in college English instruction by providing personalized and interactive learning experiences. These algorithms can transcribe spoken English passages, allowing students to receive immediate feedback on their comprehension and pronunciation. Additionally, they can generate exercises tailored to individual proficiency levels, offering targeted practice in areas needing improvement. Incorporating speech recognition technology into listening instruction not only promotes active engagement but also enables instructors to track students' progress more effectively. This paper proposed a novel approach leveraging Generative Adversarial Networks (GANs) within an Optimized Edge Computing (OEC) framework to enhance College English Listening Instruction through speech recognition. Traditional methods for English listening instruction often face challenges in providing authentic and personalized learning experiences. To address these limitations, we harness the power of GANs to generate synthetic speech data that closely resembles real-world English speech patterns. By integrating GANs with OEC, we achieve efficient processing of speech data at the edge, minimizing latency and bandwidth consumption while ensuring real-time feedback and interaction. Through extensive experimentation and analysis, demonstrate the superiority of our GAN-OEC framework over traditional baseline models and other speech recognition algorithms. Results demonstrated that the proposed model achieves an average accuracy of 90.6%, a fluency score of 8.5 out of 10, and a pronunciation accuracy of 84.6%. These results highlight the transformative potential of GAN-OEC in revolutionizing English instruction and language learning outcomes in educational settings.

Course Design Based on Association Rule Mining in Blended Teaching of College English

Course design based on association rule mining in blended teaching of college English offers a data-driven approach to curriculum development, enhancing the effectiveness of English language instruction. By analyzing patterns and relationships within student performance data, association rule mining algorithms identify correlations between different learning activities, topics, and assessment outcomes. This information enables instructors to design courses that strategically integrate online and offline learning components, ensuring a cohesive and engaging learning experience. Additionally, association rule mining facilitates the identification of potential dependencies between course elements, allowing for the optimization of teaching sequences and resource allocation. This paper investigates the use of the Extraction Apriori Rule Mining Blended (EARMB) technique to enhance teaching effectiveness in English instruction within blended learning environments. Through the analysis of association rules derived from instructional data, significant relationships between instructional components and student outcomes are identified, providing valuable insights for course design and instructional strategy development. A structured course design framework is presented, integrating various instructional activities and assessment methods to address diverse language skills and learning objectives. Additionally, a comparative analysis of different teaching approaches, including traditional classroom instruction, online learning platforms, and blended teaching, demonstrates the efficacy of blended teaching in promoting student engagement, improving learning outcomes, and enhancing overall student satisfaction.

A Comparative Study on the Results of College English Grade 4 Based on Multi-model Prediction

Predictive analytics improves educational outcomes by offering insights into student performance and influencing targeted interventions. In this work, we undertake a detailed comparative analysis of predictive models for projecting College English Grade 4 exam results, to contribute to the improvement of educational predictive analytics. We compare the predicted accuracy and interpretability of several modeling approaches, including ensemble learning methods like Random Forests, interpretable models like Decision Trees, and Support Vector Machines (SVM). We evaluate each model's performance using important metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) coefficients. Our results show that Random Forests have greater prediction accuracy, beating other models in terms of MSE, RMSE, and MAE. Despite its slightly lower R2 coefficients, Decision Trees perform competitively and provide useful insights into crucial variables. In contrast, SVM shows limitations in prediction accuracy for College English Grade 4 exam results. We highlight the relevance of these findings for educators and administrators, emphasizing the need to make educated decisions when selecting predictive models and implementing targeted interventions to promote student achievement. This work advances the field of educational predictive analytics by giving empirical evidence for the efficacy of various modeling methodologies and emphasizing the importance of model interpretability in understanding student performance determinants. More study is needed to improve predictive analytics methods and enhance their applicability in educational settings.

Exploring the Optimization Model of University English Blended Teaching Mode Combined with Deep Learning

With university English blended teaching mode combined with deep learning marks a significant advancement in language education. With integrating deep learning techniques into blended teaching approaches, educators can leverage vast amounts of educational data to enhance the effectiveness and personalization of English language instruction. Deep learning algorithms analyze student interactions, performance data, and learning preferences to tailor instructional content and activities to individual needs. This model enables educators to create dynamic and adaptive learning experiences that blend traditional classroom instruction with online resources and digital tools. The paper presents a comprehensive examination of blended teaching methodologies within the realm of English education, integrating principles of deep learning to enhance learning outcomes. Through a series of experiments and analyses, the study investigates the effectiveness of various teaching components, the utilization of online resources, and the application of diverse deep learning architectures. Findings underscore the significance of a balanced integration of traditional pedagogies and digital resources, with online materials emerging as a pivotal aspect of the teaching strategy. The study conducted experiments evaluating the effectiveness of blended teaching methodologies in English education, with numerical scores ranging from 4.2 to 9.0 across different parameters. Results indicated that experiments with higher scores for traditional teaching effectiveness (ranging from 6.5 to 9.2) and deep learning integration (ranging from 3.9 to 9.1) tended to yield superior overall learning outcomes (ranging from 4.3 to 9.0). Furthermore, online resources received consistently high weighted scores (ranging from 7.2 to 9.5), underscoring their significant contribution to the overall teaching strategy.

Practice and Exploration of University English Teaching Reform Based on Virtual Reality Technology

The university English teaching, the integration of virtual reality (VR) technology represents a groundbreaking frontier poised to revolutionize traditional pedagogical approaches. As education evolves to meet the demands of a digital age, incorporating immersive technologies like VR offers transformative opportunities to enhance learning experiences and outcomes. In the dynamic landscape of university English teaching, the integration of cutting-edge technologies like Augmented Virtual Feature Classification (AVFC) heralds a transformative era of pedagogical innovation. AVFC represents a pioneering approach that harnesses the power of virtual reality (VR) technology to reshape traditional teaching methodologies. By leveraging the power of VR, AVFC redefines traditional teaching methodologies by immersing students in virtual environments enriched with augmented features. These features augment the learning experience by providing interactive elements, visual aids, and simulated scenarios tailored to enhance language learning. The implementation of AVFC has yielded remarkable results, demonstrating its effectiveness in driving positive learning outcomes. By immersing students in virtual environments enriched with augmented features, AVFC offers an unprecedented level of engagement and interactivity in language learning. AVFC implementation has demonstrated a 30% increase in student engagement and a 25% improvement in language proficiency compared to conventional teaching methods, highlighting its effectiveness in driving positive learning outcomes.

A Study of Innovative College English Teaching Model for Global Competence

A Study of Innovative College English Teaching Model for Global Competence

Problem-Based Learning in College English Teaching in the Internet Era

Problem-Based Learning in College English Teaching in the Internet Era

College English Translation Teaching with the Goal of Improving English Application Ability

College English Translation Teaching with the Goal of Improving English Application Ability