The Design and Application of College English-Aided Teaching System Based on Web

Chen, Rui

doi:10.1155/2022/3200695

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…Chen [ 20 ] conducted research and developed a proposal for a web-based system for university English-assisted teaching in order to enhance resource sharing and scheduling. A web server and an Android mobile terminal are used to implement the system, and a web-based data integration module is created.…”

Section: Related Workmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students’ internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students’ solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.

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Section: Related Workmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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“…Edge computing has emerged as a powerful complement to big data analytics, especially in contexts where realtime processing and low-latency responses are critical, such as in the realm of Internet of Things (IoT) devices and sensor networks [17]. By bringing computational power closer to the data source, edge computing minimizes latency and bandwidth requirements, making it an ideal solution for processing and analyzing data at the edge of the network, where it is generated [18].…”

Section: Introductionmentioning

confidence: 99%

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Lei Yu, Yunyun Zhu

2024

jes

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Big data mining in English teaching revolutionizes language instruction by leveraging vast amounts of data to personalize and optimize learning experiences. This approach utilizes sophisticated algorithms to analyze learners' language usage patterns, comprehension levels, and areas of difficulty. This paper presents a novel Quality Improvement Model of English Teaching (QIMET) tailored for universities, integrating big data mining techniques with a Stacked Hashing Edge Computing Model (SHECM). Recognizing the importance of enhancing English language proficiency among university students, particularly in an increasingly globalized educational landscape, this research aims to optimize English teaching methodologies through advanced computational approaches. The proposed QIMET framework leverages big data mining to analyze extensive datasets of student performance, language usage patterns, and instructional effectiveness. By extracting valuable insights from these datasets, educators can identify areas for improvement, tailor teaching materials, and personalize learning experiences to meet individual student needs. Simulation analysis of Across a dataset of 500 university students enrolled in English language courses, QIMET achieves an average improvement of 30% in language proficiency scores compared to traditional teaching methods. Moreover, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 25%, grammar comprehension by 20%, and communication skills by 35%. The integration of the SHECM enhances the computational efficiency and scalability of the QIMET framework. Real-time data processing and analysis enable educators to make timely interventions and adjustments to teaching strategies, resulting in more responsive and effective English language instruction.

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“…Technology, a silent partner, offers avenues for exploration and expression, bridging worlds beyond the classroom walls [5].In the modern English classroom, an innovative interaction model merges seamlessly with machine learning, revolutionizing the landscape of education. As students enter, personalized learning algorithms spring into action, analyzing individual strengths, weaknesses, and learning styles [6]. Armed with this insight, the teacher, now equipped with AI-assisted tools, tailors instruction to meet the unique needs of each student.…”

Section: Introductionmentioning

confidence: 99%

Optimization of English Classroom Interaction Models Incorporating Machine Learning

Ren

2024

jes

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The optimization of English classroom interaction models incorporating machine learning represents a paradigm shift in language education, offering innovative ways to enhance student engagement, participation, and learning outcomes. With machine learning algorithms, educators can analyze various factors such as student proficiency levels, learning styles, and behavioral patterns to dynamically adapt classroom interactions in real-time. These models can predict and optimize the timing, content, and format of instructional activities, fostering more personalized and effective learning experiences.This paper explores the implementation of optimized interaction models and machine learning predictions in the English classroom to enhance student engagement, academic performance, and personalized instruction. Through the analysis of various tables and findings, key insights emerge regarding the effectiveness of student-centered learning, active learning strategies, differentiated instruction, and technology integration within the interaction model. Additionally, machine learning predictions offer valuable opportunities for personalized instruction based on predicted proficiency levels. The study demonstrates promising outcomes, but limitations such as sample size constraints and data quality issues must be addressed to ensure the reliability and applicability of the findings. Results indicate a 15% increase in student participation rates, a 12-point rise in average test scores, and a shift from high to moderate teacher intervention frequency. Additionally, machine learning predictions achieved an accuracy rate of 80%, with 8 out of 10 correct predictions. Aspect evaluation scores revealed high effectiveness in student-centered learning (9.2), formative assessment (9.3), and teacher facilitation (9.0). These findings contribute to enhancing teaching and learning practices, supporting educators in fostering more engaging and personalized learning experiences for students.

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The Design and Application of College English-Aided Teaching System Based on Web

Cited by 10 publications

References 20 publications

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Optimization of English Classroom Interaction Models Incorporating Machine Learning

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