English teaching evaluation based on reinforcement learning in content centric data center network

Guo, Hongyu; Jiang, Xiaoyan

doi:10.1007/s11276-021-02868-9

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…First, a point 𝑢 is randomly selected as the cluster center 𝑒 , and then the feature distance between other unselected points 𝑢 ′ and 𝑒 is calculated. Euclidean distance expresses the feature distance, and the calculation formula is shown in equation (4). The point farthest from 𝑒 is chosen as 𝑒 .…”

Section: Student Groupingmentioning

confidence: 99%

“…Although traditional English teaching methods are valuable in providing a foundational understanding of the language, they often need help to fully prepare students for real-world communication in higher vocational education. These methods typically rely on standardized curricula, traditional classroom settings, and one-size-fits-all instructional materials, which can limit their effectiveness in meeting the specific needs and contexts of students pursuing vocational careers [3][4].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Song

2024

J Multimed Inf Syst

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In higher vocational English teaching, delivering teaching materials promptly and enabling instantaneous feedback is crucial for effective language learning. This paper presents a novel algorithm applying Adversarial Autoencoders (AAE) to reduce latency in 5G networks. The proposed algorithm integrates the characteristics of small base station collaboration, multicast, and predictable user behavior, namely the AAE-based collaborative multicast proactive caching scheme (AAE-CMPC). Initially, students are grouped into different preference clusters based on their characteristics. Subsequently, the AAE technique is employed to predict the content that each group might request. To reduce the redundancy of cache content, an ant colony algorithm is used to pre-deploy the predicted content to each small base station to realize the collaboration between small base stations. The proposed AAE-CMPC scheme demonstrates superior performance when compared to three benchmarks. The simulation results indicate that an increase in the storage capacity of the macro base station leads to a reduction in loss rate, and it can be attributed to the enhanced cache hit ratios achieved through proactive caching. The AAE-CMPC algorithm revolutionizes higher vocational English teaching by reducing latency and enabling instantaneous feedback. Students can access teaching materials promptly, receive real-time feedback on their progress, and engage in collaborative activities seamlessly. The framework also leverages edge computing, allowing for increased storage capacity, scalability, and reliability, resulting in an enriched learning experience.

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Section: Student Groupingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Song

2024

J Multimed Inf Syst

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“…Fuzzy comprehensive evaluation is one of the basic methods of engineering fuzzy systems, also known as fuzzy multi-objective decision-making IX. The fuzzy comprehensive evaluation method has a wide range of applications [3].…”

Section: Research Status Of Fuzzy Comprehensive Evaluation Modelmentioning

confidence: 99%

Evaluation of the Effect of Ideological and Political Education in Colleges and Universities Based on Fuzzy Comprehensive Evaluation Model

Gao¹,

Hu²,

Tian³

2023

Atlantis Highlights in Computer Sciences

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The so-called fuzzy comprehensive evaluation refers to the fundamental method of engineering fuzzy systems, which is widely used. For example, it plays a very important role in quality control, performance evaluation, weather forecast, expert scoring system, medical diagnosis, economic management, psychological measurement and other fields. Fuzzy comprehensive evaluation has the following three links: determine the fuzzy relationship matrix, determine the weight, and select the operator. Gradually, with the rapid development of fuzzy comprehensive evaluation, the above links have been further improved in the continuous practical application. For example, the weight is corrected by the method of entropy, and the descending semi-trapezoid is replaced by changing the membership function to determine the fuzzy relation matrix. In terms of operators, power operators and nonlinear operators have also appeared. Through the research and analysis of a large number of relevant materials, this paper has a full understanding and grasp of the development information of fuzzy comprehensive evaluation. And carried out a very detailed overview and summary, and further improved the integrity and diversity of the fuzzy comprehensive evaluation method.

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Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

Fan,

Wang

2024

IFS

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The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

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English teaching evaluation based on reinforcement learning in content centric data center network

Cited by 3 publications

References 25 publications

Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Evaluation of the Effect of Ideological and Political Education in Colleges and Universities Based on Fuzzy Comprehensive Evaluation Model

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

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