Mobile English Interactive Teaching Design Method Based on Deep Learning

The intelligent use of digital teaching resources and the deep penetration of information technology can help promote the change and development of English teaching and improve the sharing, interactivity, interestingness and effectiveness of English translation teaching resources. This paper develops an intelligent classroom teaching model for English translation that combines scaffolding and multimodal discourse teaching using the U Campus teaching platform. To create an intelligent learning environment for English translation, this paper uses distributed crawler technology to crawl English teaching resources and Minhash algorithm to de-weight and optimize the data, constructs an English translation corpus, and introduces a lightweight Transformer network to establish a machine translation model. This paper quantitatively analyzes the teaching effect of the English translation smart classroom teaching model using data from University C students as the research object. The results of the study show that the students in class A1 scored 95.02 points in topic comprehension, the translation theory and skills improved by 8.46 points compared with the students in class A2, and there was a significant difference between the students’ cross-cultural communication skills at the 1% level. By integrating the information technology platform with English translation teaching, students’ translation skills can be enhanced, they can learn more English resources, and their intercultural knowledge and competence can be improved.

Human-autonomous has begun to develop into complex system control and advanced intelligent control, and is widely applied to various fields such as national defense, scientific research and economics. The design scheme of an English network teaching system based on artificial intelligence and WBIETS system is proposed in this paper. The transmission and real-time scheduling of English network teaching resources is realized through network supporting layer. The fuzzy clustering algorithm is used for English teaching resource scheduling. In the process of information retrieval, the MAC link reconfiguration model is used for information fusion. The artificial intelligence algorithm is used in the cloud computing environment to carry out the adaptive retrieval and fuzzy control of the English network teaching resources information, and the optimization design of the resource scheduling algorithm is realized. The ActiveX component object model is used to allocate and optimize English network teaching resources, and complete the software development and design of the system. Test results show that the design of the English network teaching system with the proposed method is good for the real-time performance of the English teaching resource scheduling, the resource clustering and distribution ability is strong, the output error rate is reduced, and the artificial intelligence configuration ability of English network teaching is improved.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

This article uses artificial energy to analyze literature reading and cases in English teaching of vocational skills training analyzes the current situation of artificial intelligence technology and colleges and universities and proposes the application of artificial intelligence technology to vocational skills training. To improve the quality of teaching in English teaching, we researched on English teaching in artificial intelligence from a theoretical perspective. Through literature research, we find the matching point between constructivism theory and artificial intelligence-assisted teaching, and through the second language acquisition theory and communicative teaching method to summarize the language acquisition process assisted by artificial intelligence. Based on the constructivist teaching theory, with the help of the 4 C/ID model idea, combined with the characteristics and functions of artificial intelligence, artificial intelligence, and traditional teaching activities are organically combined to design a teaching design model of artificial intelligence assisted English teaching. It designed an integrated management system for English teaching resource information based on artificial intelligence. The hardware design of the system is realized relying on the design of the English teaching resource storage module and system server design. Based on the design of the system operating environment and database design, the software design of the system is realized. The results show that the proposed integrating management system of English teaching resource information is more suitable for sharing English teaching resource information than the conventional integrated management system of English teaching resource information. The combination of artificial intelligence and English teaching will surely bring breakthroughs in the development of high-intelligence teaching. The potential of artificial intelligence in the field of education is unlimited. As with the impact of computer technology on education reform, we have reason to believe that AI-assisted instruction will become an important direction of modern educational technology.

Aiming at the problem of the high comprehensive evaluation index of recall and precision in the traditional personalised recommendation methods of college English online teaching resources, a personalised recommendation method of college English online teaching resources based on hidden Markov model is proposed. First, extract the label features, student learning behaviour features, and time weight features. Then, pre-process the extracted college English online teaching resource data, build a recommendation model based on the pre-processing results, and use a hidden Markov model to process the hidden data to obtain the maximum likelihood estimates the parameter. Finally, input the parameter into the recommendation model to obtain the optimal parameter, that is, the optimal recommendation result. The simulation results show that the comprehensive evaluation index values of precision and recall of the recommended results of the proposed method are within 0.9 and 0.5, which has a good recommendation effect and meets the needs of the development of network teaching.

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

In order to improve the quality of blended English teaching, a personalized recommendation system of blended English teaching resources based on deep learning is proposed. Analyze the functional architecture of the system, and design user registration module, crawler module and personalized recommendation module of teaching resources. It is used for user registration, crawling of blended English teaching resources and personalized recommendation of teaching resources. The system software part calculates the user interest similarity, and uses the neural network in deep learning to calculate the interest preference data, so as to obtain the recommendation results of teaching resources. The experimental results show that compared with the traditional teaching resource recommendation system, this system can accurately calculate the user interest similarity, so as to improve the recommendation accuracy, so it obtains higher user satisfaction.

The traditional English teaching system has certain problems in the acquisition of teaching resources and the innovation of teaching models. In order to improve the effect of subsequent English online teaching, this paper improves the machine learning algorithm to make it a core algorithm that can be used by artificial intelligence systems. Moreover, this paper combines the WBIETS system to expand the system function, analyzes the needs of the English network teaching system, and constructs the system function modules and logical structure. The data layer, logic layer, and presentation layer in the system constructed in this paper are independent of each other and can be effectively expanded when subsequent requirements change. In addition, this paper solves the problem of acquiring English teaching resources through the WBIETS system. To evaluate the performance of the English network teaching system, this paper performs comprehensive mathematical and experimental analysis. The experimental results show that the system constructed in this paper basically meets the actual teaching requirements.

In order to improve the efficiency of teaching resource integration, this paper designs an English multimedia network teaching resource integration system based on cloud storage. The hardware design completes the design of FPGA core board and the selection design of embedded microprocessor. In the software part, the functional module architecture of cloud storage system and the multimedia network resource integration database under cloud storage system are designed. The test results show that compared with the English teaching resource integration system without cloud storage test group, the coupling coefficient of the resource integration system designed in this paper is relatively high. It shows that the system has a strong ability to integrate English multimedia network teaching resources. At the same time, under the auxiliary cloud storage technology, the system has wider coverage and higher resource storage capacity, which has practical application value.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

Resource allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

The study takes English exercises in English teaching resources as the starting point, and combines cognitive diagnosis theory to assess students' knowledge and ability levels. On the basis of integrating the traditional collaborative filtering algorithm, the sorting learning method is introduced, and the combination of the two becomes a hybrid collaborative filtering algorithm. The results show that the accuracy of the proposed hybrid collaborative filtering algorithm under cognitive diagnosis is as high as 98%, with stable performance in accuracy, F1 value and recall rate. The results outperformed the collaborative filtering algorithm, providing learners with English teaching resources that are more in line with their cognitive ability. The English exercises recommended by the algorithm have better learning effects than those recommended by the collaborative filtering algorithm, effectively providing learners with personalised English teaching resources. In practice, the English exercises recommended by the algorithm were more effective than those recommended by the collaborative filtering algorithm.

This article examines teaching resources addressing the Partition of India, exploring how Partition is framed through supplementary teaching resources developed by government and non-governmental organisations in England. Using critical discourse analysis, the study investigates the National Archives teaching resources on Partition and other educational initiatives led by non-governmental organisations called the Partition History Project and the White Line Project. Drawing on McQuaid and Gensburger’s concepts of the administration of memory and Bernstein’s regulative discourse, the analysis reveals how Partition is often presented within a framework of British values, transforming a complex episode of imperial violence and migration into a moral lesson on tolerance and social harmony. Despite growing calls for decolonisation, the Partition teaching materials in England reflect an institutional preference for national coherence over critical historical inquiry, aligning with a broader tendency to marginalise colonial histories or recast them as narratives of shared heritage. By examining how Partition is selectively remembered and pedagogically framed, this article argues for a more critical, inclusive and historically grounded approach to teaching materials addressing the British Empire in English schools.