Computer-Assisted Instructional Strategies for English Linguistics Courses in a Diverse Educational Management Environment
Publié en ligne: 19 mars 2025
Reçu: 09 nov. 2024
Accepté: 08 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0403
Mots clés
© 2025 Li’ao Luo, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
With the popularization of computers and networks, multimedia technology and the Internet provide broader prospects and richer resources for school education [1–2]. The new teaching mode should be supported by modern information technology, especially network technology, so that the teaching and learning of English can be independent of time and place to a certain extent, and develop in the direction of personalized and independent learning [3–4]. It can be seen that the language teaching mode based on multimedia and network has gradually become the new trend of foreign language teaching [5]. At present, for foreign language teachers, to be able to design and implement classroom teaching activities in the machine-assisted environment and make reasonable and effective assessment of students’ performance, so that students can master and apply English in a more natural language learning and communicative environment, and thus cultivate compound high-level English talents more effectively, this is a higher professional requirement put forward by the times for English teachers [6–8]. However, due to the heavy teaching load, lack of teaching aids and equipment, and relatively lagging behind in teacher training, there are large regional differences in the development of computer-assisted English language teaching activities in China [9–10].
Computer-assisted language teaching focuses on guiding students to learn the language with the help of computers, seeks the best learning effect through the organic combination of teaching content, teaching process and computer assistance, realizes the organic combination of classroom teaching and network teaching [11–13], stimulates the students’ interest in and motivation for learning, helps to cultivate their ability for independent learning and sustainable development, improves the students’ quality in a comprehensive way, and helps to alleviate the rapid increase in the number of students, which is the most important factor in the development of English language teaching. It helps to alleviate the contradiction between the rapid increase of student groups, the increasing scale of teaching and the lack of high-quality teaching resources and the shortage of teachers, and provides a kind of support from the aspect of “technological sophistication” for solving the problem of “large volume and wide range” of college English teaching [14–16]. The Teaching Requirements for English Courses in Universities clearly puts forward that the new teaching mode of English in universities should be the English multimedia teaching mode based on computers and classrooms. Scholars try to apply computer-assisted multimedia teaching technology to all aspects of teaching. Such as listening teaching, writing teaching, language testing and so on. To summarize, we talk more about the advantages of multimedia network teaching, but think less about the problems behind the advantages [17–20].
This paper discusses the theoretical basis of computer-assisted language teaching and the advantages of computer-assisted English linguistics courses. According to the arrangement of the English Linguistics course, teaching sessions are designed separately, including classroom introductions, teaching questions, and so on. Cloud resource management technology is proposed for managing multimedia data characteristics. Set cloud resource scheduling objectives, apply reinforcement learning to cloud resource scheduling problems, and develop a cloud resource scheduling strategy that is based on reinforcement learning. Analyze the optimal values of each parameter in the model and compare them to the baseline algorithm. Combine the pre- and post-test scores of the experimental and comparison classes to analyze the role of computer-assisted English linguistics courses.
Computer-assisted foreign language teaching as a modern educational technology tool is a multifunctional and up-to-date teaching tool [21–22]. It makes individualized learning and discovery learning possible. It enables the realization of modern student-centered, learning-centered and taskcentered teaching concepts, makes human-computer interaction possible, and provides the necessary tools for the updated concept of foreign language teaching and the need to optimize the means of foreign language teaching.
Theories of computerized educational applications heavily rely on learning theories and information technology. Behaviorist, cognitivist, and constructivist learning theories lay the foundation for the use of computers in education. Constructivist learning theory In recent years, with the increasing popularity of multimedia technology, constructivist learning theory has gradually attracted widespread attention, according to the constructivist learning environment for the pilot study of education reform in schools is also increasing. Constructivism emphasizes the role of active participation, flexibility, contextual, and other elements of knowledge value in knowledge education and learning. It is believed that knowledge is acquired by learners in a certain context with the help of others, using the necessary learning materials, and through the construction of meaning. Multiple Intelligences Theory The core of the multiple intelligences theory is to emphasize the importance of taking individual differences seriously, and to regard the intellectual structure of each individual as multidimensional and open, and to consider their intelligence as a potential to be activated and cultivated by the environment and education. Computer-assisted teaching, which combines the theory of multiple intelligences with personalized English teaching to cultivate students’ multiple intelligences, provides a new teaching method to overcome the shortcomings of traditional teaching. It is in the form of illustration, sound, image, motion, and stillness, and with its extraordinary expressive power across time and space, it greatly enhances people’s understanding and feeling of abstract things and processes.
English Linguistics course is a language theory course, which is to think rationally about the phenomenon of English as a language, to teach the knowledge of the essence and commonality of English as a language, and is a compulsory course for English majors. It is of great significance to improve students’ professional quality, academic ability and analytical and discursive ability, and it is an important link for students to improve their theoretical literacy after the basic stage of language training courses.
The language object studied in the English Linguistics course is English, which is a theoretical system interwoven with a series of specific methods and related concepts for the understanding of English and the study of English based on this understanding. It involves studying the general characteristics of language, studying its internal structure, and studying its external environment of use. It is closely related to the courses of English and American literature, pedagogy, phonetics, grammar, vocabulary, testing, pragmatics, essay writing, and so on. Not only that, English linguistics courses can also provide theoretical foundation for other related courses, and the teaching contents can even be interpenetrated and exchanged.
Attracting students’ attention and stimulating their interest
The computer is capable of comprehensive processing of images, graphics, text, animation, and sound effects. Because of the flexible and convenient characteristics of computer processing, teachers can flexibly use computers to create graphic scenarios in teaching and show students sensible pictures and pleasant melodies.
Create a vivid classroom environment to improve classroom efficiency
Under the background of “Internet+”, multimedia combines the functions of picture, sound and animation. Students can acquire more knowledge in the same time through visual and auditory stimulation, which improves the learning efficiency.
Benefit learners to deepen their memory and easy to understand knowledge
Combined with multimedia technology to integrate sound, images, text, visualization and auditory knowledge will make it easier for students to understand, especially the more abstract knowledge points.
Increase the interactivity of the classroom and promote students’ independent learning ability
Under the background of “Internet +”, multimedia computer-assisted English teaching can prompt students to think actively. Eyes, ears, mouth, hands and brain are used to learn, so that the whole learning process is more independent.
Closer communication between teachers and faster resource sharing
With the help of “Internet +”, experts and teachers and teachers can communicate efficiently and realize the high degree of resource sharing.
With the help of “Internet +”, experts and teachers and teachers can communicate with each other efficiently, realize the high degree of sharing of resources, introduce more freshly baked English materials into the classroom, and improve the effectiveness of teaching.
Breaking through the limitations of the classroom and making full use of fragmented time
After the application of “Internet + technology” in the English classroom, the traditional classroom has been endowed with Internet characteristics, such as the flipped classroom is a new model created through the Internet.
Classroom teaching is the main channel for providing comprehensible language input to students, and the improvement of the quality of talent depends largely on the level and quality of classroom teaching. Summarizing the practice and experience of using multimedia computer-assisted teaching, we present this paper’s setup for teaching English linguistics courses using computer-assisted technology. Utilize multimedia computer to introduce the new lesson. For college students, they have a strong curiosity and desire for knowledge. Interest in learning is often the direct cause of their success in learning. The rational use of multimedia teaching tools can better stimulate students’ interest in learning a few slides, a recording, a video, a few images, can be used as a material to introduce a new lesson. Introducing background knowledge, cultivating students’ cross-cultural awareness and cross-cultural communication skills. The characteristics of English subject teaching determine that there is a cultural aspect to English education and teaching. Foreign language teaching should take the ultimate goal of cultivating learners’ ability to communicate in a foreign language. Multimedia technology applied to teaching can provide students with a large number of real, intuitive, and perceptual materials. It visualizes and concretizes the teaching content, enriches students’ emotional awareness from different angles, and helps them construct the meaning of learning. Students hear and see the closest to the real situation or the original face of the things learned, and can quickly understand and master them. For example, when teaching about New Zealand, we downloaded many pictures and related materials from the Internet to reflect on New Zealand’s geography, climate, scenery, and customs. The Maori people and their culture, habits, lifestyle, and history, which were unfamiliar to the students, are gradually becoming familiar. Use multimedia to enrich the sensibility In English learning, it is extremely important to fully mobilize students’ sense of sight and hearing. Combined with traditional media, the use of multimedia means to provide a large number of images, intuitive and perceptual materials. From visual and auditory channels, with sound, images, animation, and other channels, to stimulate students’ senses and help them understand the meaning of knowledge. Use multimedia to create a real language environment and develop skills. In multimedia-assisted English reading teaching, there are three main steps: scenario presentation, form of representations, and creation of associations. Understanding, deepening, and reproducing the scene. Enrich imagination-memorize-store. The use of multimedia to guide students to think and promote the development of student thinking. Multimedia computer-aided instruction is long on dynamic, image, intuitive. And short of abstraction, analysis, summarization. Therefore, in multimedia teaching, in the image of an intuitive presentation, explaining the completion of the teacher’s analysis and summary will play a role as a finishing touch. Teachers should be good at analyzing and digging into the textbook, and try to create a situation in the classroom that stimulates students’ motivation to learn. The complexity of the learning task needs to be decomposed, so as to facilitate the students’ understanding of the gradual introduction to in-depth. Use multimedia to consolidate knowledge The application of multimedia courseware can realize the “multi-information, high-density, fast-paced” in English teaching. This feature is particularly evident in the revision class. The use of multimedia courseware, whether it is a summary of knowledge or a general review before the exam, is convenient to reflect systematic, connected, and regular knowledge. And it can greatly increase the amount of classroom information. At the same time, teachers can also save time for the big children’s board, becoming an important way to solve the conflict between school hours.
Multimedia networks not only have the function of processing various media information and humancomputer interaction. More importantly, it enables online transmission of multimedia information and sharing of multimedia resources, creating an ideal multimedia network teaching environment. Therefore, it is necessary to make full use of network resources to carry out colorful extracurricular activities. Carry out English research study and cooperative learning Utilize multimedia to carry out independent learning outside the classroom
Multimedia integrates text, sound, graphics, images, animation, and video into one, so that it can express colorful teaching sessions in a more natural and realistic way. Graphics, text, and sound fully reflect the integrated nature of multimedia.
Multimedia data is actually made up of various types of data. It usually includes different types of data such as text, graphics, images, sound, video images, animations, and so on. And the same type of data can be represented in different ways. The complexity of multimedia data is not limited to the creation, storage, retrieval, and data processing techniques.
Multimedia data types are both complicated and informative. The sound and video image data in multimedia data are time-related information. Many occasions require real-time processing, such as real-time compression and decompression, transmission, and synchronization of sound and video image information. In addition, interactive operations such as editing, retrieval, display, and others. need to have a real-time operating system.
Due to the diversity of multimedia data, the development of multimedia applications requires the intervention of a variety of professionals, including computer developers, text writers, and other aspects of personnel collaboration. As a result, the original material is often distributed in different space and time, which makes the establishment and management of distributed multimedia databases and the application of multimedia communication become the key technology to the multimedia computing system.
Cloud Computing Resource Scheduling Objective
The goal of this paper is to rationally generate different types of virtual machine resources through efficient scheduling, so that the system has low energy consumption while maintaining high QoS.
In this discussion of scheduling problem, the following cloud computing resource scheduling system is considered: the system has k different kinds of physical resources, including computing resources CPU, storage resources memory and external memory, network bandwidth and so on. These device resources are provided to the user in the form of virtual machines through virtualization technology, and it is assumed that the type of virtual machine required and the time taken for each task request are determined.
According to the characteristics of cloud computing, the system can create multiple virtual machines of different system types on a single physical machine. And it can create and destroy them in real time as needed. The system divides virtual machines into
Let
In the scheduling problem under discussion, the objective is to rationalize the creation of VMs such that user QoS is maximized and resource energy consumption is minimized, subject to the constraints of Eq. (1). In summary, the problem is modeled as an optimization problem as described below:
Where
It is necessary to focus on an important non-functional requirement Service Level Agreement (SLA) related to QoS, which is important for the ecology of cloud computing resources. Considering that the research content of this paper mainly focuses on cloud computing resource scheduling algorithms, QoS is simplified to response time and the objective function is further formulated as:
Where
Modeling of cloud computing resource scheduling task
In a cloud computing system, multiple virtual machines often cope with multiple different cloud computing tasks, so it is necessary to consider the scenario of multiple service platforms. Common multi-service platform queuing models have
When the system is in a steady state, Cottle’s law can be used to calculate the average queue length and average waiting time. In the model where the number of VMs is S, the request process of cloud users follows a Poisson process with an arrival rate of
The cloud computing resource scheduling problem and the resource allocation process are modeled as the implementation details of a Markov decision process. In this paper, it is argued that the resource scheduling process satisfies the Markov property. The MDP can be described in terms of quintuple:
In order to be able to apply reinforcement learning to the cloud computing resource scheduling problem, it is necessary to transform the cloud computing resource scheduling problem into a Markov decision making process, and therefore it is necessary to give a representation of the system’s environment, states, actions, rewards and policies.
The state is defined as a vector which consists of the current number of each resource of the system, the amount of different types of virtual machines retained and the amount of resources consumed by different types of virtual machines is defined as follows:
The reward value received by the system is defined using the reward function given in Equation with the ultimate goal of maximizing the overall cumulative reward value. I.e:
Where
Task Preprocessing
In the scheduling process, the execution order of tasks is different, the selected resources will be different, and the required completion time including the waiting time of the task and the execution time of the task will be affected, thus affecting the process of the whole scheduling process.
In large-scale heterogeneous cloud systems, randomly arriving tasks will have different lengths, different deadlines and different waiting times. Therefore, in this paper, the following three parameters are comprehensively considered to design the dynamic prioritization of tasks. That is, task length, task deadline, and task age.
As a result, the priority
Where
Cloud resource scheduling strategy based on reinforcement learning
The ultimate goal of the Q-Learning algorithm is to achieve an optimal policy, and each of its state-behavior pairs corresponds to a desired cumulative reward known as the Q-value, i.e., the value function [23–24]. The Q-value correction formula for single-step Q-learning is shown below:
During each scheduling selection action decision, the intelligent body uses some kind of strategy to select the action and speeds up the convergence by continuously updating the state space so that it converges to the optimal direction faster, making its value function (i.e., the Q-value) constantly approaching towards the optimal direction.
The Q-Learning algorithm is better suited for adaptive scheduling. The solution of the optimal policy for resource scheduling depends heavily on the exact definition of the state, action, and reward/punishment functions. In this paper they are defined respectively as:
State space (S)
State can be defined as the situation of each VM performing a task and can be represented by a vector.
Action space (A)
For the
Reward and punishment function (
In this paper, from the perspective of improving resource utilization and reducing energy consumption, we design the reward and punishment function by reducing the waiting time of tasks under the constraint of deadline time, which is expressed by the following equation:
For a given current task, if the current task is assigned to a virtual machine, the average utilization of the physical machine on which the virtual machine is located is higher than the average utilization of the other virtual machines to which it is assigned. Also the waiting time of the VM to which the task is assigned is less than the original waiting time and satisfies the SLA or QoS constraints, the scheduler receives a positive reward with a value of 1. If the objective function is not satisfied and the response time of the task violates the SLA or OoS constraints, it is penalized with a value of -1. Otherwise, it receives 0.
In order to evaluate the effectiveness of the scheduler, six aspects will be evaluated: energy consumption, response time, SLA violation rate, cost, task execution and total number of tasks completed. The baseline algorithms are described as follows:
LR-MMT: Dynamically scheduling workloads based on local regression and minimum migration time heuristics for overload detection and task selection respectively. DDQN: Reinforcement learning approach based on double deep Q learning. A3C-FULL: A deep reinforcement learning method for fully connected neural networks based on policy gradient.
The results of the experiments with different hyperparameter settings are shown in Table 1, by setting one of the hyperparameters
Experimental results of different hyperparameter Settings
| Hyperparameter setting | Total energy consumption (108 |
Average task response time (ms) | SLA violation rate (%) | Assembly book (¥) | Average task completion time (103 |
The total number of tasks |
|---|---|---|---|---|---|---|
| 2.78 | 8.67 | 17.5 | 95007 | 4.57 | 1896 | |
| 3.34 | 8.23 | 19.4 | 96113 | 4.41 | 1934 | |
| 3.65 | 9.19 | 14.3 | 96742 | 3.66 | 1990 | |
| 2.89 | 8.75 | 20.7 | 95079 | 4.11 | 1745 | |
| 3.21 | 8.46 | 14.1 | 96257 | 3.87 | 1958 |
When
The average response time of the model is minimized when
When
When
The experimental results show that when the model only focuses on a certain indicator, it can make the indicator reach the optimization, but it will sacrifice some other indicators, which verifies the adaptability and correctness of the model. In order to obtain the optimal model, it is necessary to combine these indicators and set the weight of each indicator reasonably. This can reduce the loss value and improve the network. Since optimizing only one metric may fall into a local optimal solution, the optimal values of hyperparameters are obtained by optimizing each metric at the same time using the block coordinate descent method as follows, and comparisons with the baseline will be made on this basis. The optimal values of the hyperparameters are obtained here, (
The comparison with the baseline algorithms is shown in Table 2 for the scheduling results of the three baseline algorithms and the algorithm in this paper on the GWA-T-12 Bitbrains dataset with a scheduling interval of 5 minutes for a total duration of 3 days.
Comparison with the baseline algorithm
| Algorithm | Total energy consumption (108 |
Average task response time(ms) | SLA violation rate (%) | Assembly book (¥) | Average task completion time(103 |
The total number of tasks |
|---|---|---|---|---|---|---|
| LR-MMT | 2.105 | 9.32 | 7.66 | 90124 | 5.24 | 1569 |
| DDQN | 1.989 | 9.61 | 8.09 | 91085 | 5.11 | 1634 |
| A3C-FULL | 1.804 | 8.24 | 7.12 | 90057 | 4.03 | 1657 |
| Cloud resource scheduling strategy based on enhanced learning | 1.637 | 7.55 | 5.25 | 87674 | 3.26 | 1821 |
The experimental results show that DDQN and A3C-FULL, based on deep reinforcement learning, consume less energy than heuristic LR-MMT. The total energy consumption of the heuristic LR-MMT algorithm is 2.105 × 108 w. The algorithm in this paper (Reinforcement Learning based Cloud Resource Scheduling) has the lowest energy consumption.
Reinforcement learning-based cloud resource scheduling has the lowest average response time, which is 9.1% lower than A3C-FULL. Because the algorithm in this paper accurately identifies the edge nodes and cloud center nodes, and schedules tasks to the cloud center nodes only when necessary to reduce the task response time. The SLA violation rate of this paper’s algorithm is also the lowest because this paper’s algorithm fully utilizes the temporal patterns between tasks to achieve long-term intelligent scheduling. The algorithm in this paper also accurately recognizes the cost billing patterns of hosts and can ensure that tasks are assigned to as few hosts as possible. This leads to cost reduction by improving host utilization and reducing energy consumption, which results in a 2.7% lower cost compared to the best baseline A3C-FULL.
In addition, reinforcement-learning-based cloud resource scheduling has the lowest average completion time and the highest total number of task completions. The completion time of tasks in the previous scheduling interval and the expected completion time of active tasks are also accurately identified. For time-critical tasks, they are assigned to computationally powerful hosts to avoid the time overhead from migration, in order to increase the number of task completions, which is 16.06% higher than LR-MMT. In conclusion, Reinforcement Learning-based Cloud Resource Scheduling has better scheduling performance than the baseline algorithm.
Two classes were randomly selected from the sophomore English majors in a school. Both classes were taught by the same English teacher, and the English teaching content was synchronized.
Pre-test: Both classes were first taught by conventional methods (without computer-assisted) for more than two weeks. Before the experimental class entered the multimedia computer-assisted instruction, the first survey of English learning effectiveness was conducted in both classes using questionnaires.
Post-test: After the experimental classes finished the experimental content, a second survey on the effectiveness of English learning was conducted by means of a questionnaire for each of the two classes.
Academic Achievement: Before the experimental class entered the multimedia computer-assisted instruction, the two classes were tested on their academic achievement with Paper L as a pre-test. After the experimental classes finished the experiment, the academic achievement measured by PaPer 2 was used as the post-test achievement.
The first survey of English learning effectiveness was conducted after both classes were taught for two weeks without multimedia computer-assisted instruction. The statistics of the pre-test results of the two classes on the evaluation of indicators reflecting the effectiveness of teaching and learning in the English learning effectiveness test materials are shown in Table 3.
The pre-test results of the two classes are counted
| Laboratory class(N=36) | Contrast class(N=36) | |||||||
|---|---|---|---|---|---|---|---|---|
| <60 | 60-75 | 75-90 | >90 | <60 | 60-75 | 75-90 | >90 | |
| Study interest | 5 | 15 | 8 | 8 | 9 | 10 | 8 | 9 |
| Learning efficiency | 6 | 11 | 12 | 7 | 8 | 10 | 12 | 6 |
| Combined effect | 3 | 15 | 10 | 8 | 6 | 9 | 10 | 11 |
| Inspirational thinking | 8 | 9 | 10 | 9 | 9 | 10 | 11 | 6 |
| Oral expression | 8 | 11 | 12 | 5 | 7 | 11 | 12 | 6 |
| Master key | 10 | 11 | 10 | 5 | 6 | 13 | 11 | 6 |
| Written expression | 9 | 10 | 12 | 5 | 9 | 12 | 10 | 5 |
| Improve hearing | 10 | 10 | 5 | 11 | 5 | 11 | 10 | 10 |
| Focus | 8 | 13 | 8 | 7 | 8 | 10 | 11 | 7 |
The experimental class and the comparison class had different numbers of students who reached 90 points in each dimension in the dimensions of learning interest, learning efficiency, comprehensive effect, inspiring thinking, oral expression, mastering key points, written expression, improving listening, and focusing attention. However, the total mean value of the percentage of students who obtain more than 90 points in the nine dimensions of interest in learning and learning efficiency is the same for both classes, which is 0.17.
After the experimental class had completed the experimental content, a second survey on the effectiveness of English language learning was conducted for the two classes separately. The results of the survey in both classes are shown in Table 4.
The results of the survey of the two classes
| Laboratory class(N=36) | Contrast class(N=36) | |||||||
|---|---|---|---|---|---|---|---|---|
| <60 | 60-75 | 75-90 | >90 | <60 | 60-75 | 75-90 | >90 | |
| Study interest | 4 | 3 | 5 | 24 | 6 | 10 | 8 | 12 |
| Learning efficiency | 3 | 6 | 11 | 16 | 3 | 5 | 14 | 14 |
| Combined effect | 3 | 7 | 12 | 14 | 4 | 14 | 8 | 10 |
| Inspirational thinking | 4 | 8 | 9 | 15 | 6 | 4 | 15 | 11 |
| Oral expression | 3 | 5 | 12 | 16 | 7 | 10 | 6 | 13 |
| Master key | 5 | 9 | 7 | 15 | 5 | 6 | 12 | 13 |
| Written expression | 6 | 8 | 10 | 12 | 4 | 7 | 8 | 17 |
| Improve hearing | 1 | 6 | 11 | 18 | 2 | 10 | 12 | 12 |
| Focus | 3 | 7 | 9 | 17 | 4 | 11 | 15 | 6 |
The mean value of the number of students in the experimental class who achieved more than 90 points in each dimension after the computer-assisted English linguistics course-based instruction was 0.39. The greatest improvement was seen in the learning interest dimension, with the number of students scoring more than 90 points at 24, which was more than 60% of the class.
The comparison class had a mean score of 0.29 for the number of students who scored 90 or higher on the nine dimensions on the second test.
Academic performance is a significant indicator of the effectiveness of classroom teaching. For this reason, students were tested twice, once as a pre-test, before the introduction of multimedia computer-assisted teaching in the experimental class, on the content of English linguistics courses in colleges and universities. Once was a post-test, after computer-assisted English teaching in the experimental class. The average scores of the pre-test and post-test of the two classes are shown in Table 5. After the implementation of the computer-assisted English linguistics course, the English academic score of the experimental class was 93.79.
Average performance of pre-test and post-test
| Premeasurement | Posttest | |
|---|---|---|
| Laboratory class | 78.65 | 93.79 |
| Contrast class | 76.23 | 86.35 |
The comparison of single-group and equal-group experiments was employed to generate statistical results for testing large samples (n>30) in both pre- and post-test. The overall averages for the pretest and post-test for both classes are shown in Table 6.
The total average of both pre-test and post-test
| Premeasurement | Posttest | Z value | Significance P value | ||
|---|---|---|---|---|---|
| Laboratory class | Total Mean | 75.64 | 87.11 | 2.34 | 0.043 |
| Total SD | 19.52 | 15.04 | |||
| Contrast class | Total Mean | 73.83 | 80.47 | 0.37 | 0.097 |
| Total SD | 18.66 | 17.29 | |||
| Z value | 0.13 | 3.45 | P<0.05, Significant difference. | ||
| Significance P value | >0.05 | <0.05 | |||
For the single group experiment, the degree of difference in the total effect mean between the pre and post tests for the comparison class was statistically calculated as Z=0.37<1.96 (significant level taken as 0.05) and P=0.097>0.05 not significantly different.
For the two tests before and after the experimental class, the statistically calculated Z value of 2.34 is very close to the critical value of 1.96 for a significant difference, which is a greater difference. As far as the equal group experiment is concerned, there is no significant difference (P>0.05) between the total mean of the pre-test of the experimental class and the comparison class. The post-tests of the two classes, on the other hand, are significantly different, P=0.043<0.05.
The results indicate that when both classes were not using multimedia computer-assisted instruction, awareness of the effectiveness of classroom instruction was basically the same, as reflected in the test content. That is to say, the students in both classes reflected basically the same situation about the teaching of the same teacher. After the introduction of multimedia computer-assisted instruction in the experimental class, their perceptions changed significantly. This change reflects the results produced by the pedagogical advantages of multimedia computers. Therefore, on the whole, the introduction of multimedia computer-assisted teaching in traditional classroom teaching is conducive to enhancing teaching effectiveness.
Multimedia English teaching, at its current level of development and in terms of actual use in teaching experiments, still lacks the ability to use natural language for interpersonal communication. And interpersonal communication is precisely the essence of language use, so it still has certain limitations. Only by organically combining multimedia teaching into the whole language teaching process, as a necessary link and supplement to the teacher, rather than replacing the teacher, can this limitation be minimized.
Emotional communication between teachers and students cannot be realized through multimedia computer teaching. The verbal communication between teachers and students in the classroom is more contagious than the interactive communication between humans and computers in the multimedia language laboratory.
Some of the traditional English teaching methods are more effective than the multimedia teaching methods. In traditional English teaching, objects, wall charts, and sketches are often used, which have the advantages of simplicity and practicality. Through the teacher’s classroom explanation or a few minutes of demonstration experiments, the expected teaching purpose can be achieved. If this type of teaching content is converted into multimedia teaching courses, teachers may have to spend several times the amount of time creating teaching materials, which is both time-consuming and unnecessary. Purchasing pre-made courseware will also increase the cost of teaching. In addition, in the teaching of speaking and writing, traditional teaching methods also have greater advantages than multimedia-assisted teaching methods.
The above three points show that multimedia is only one of the many teaching media. The needs of teaching vary, and not every teaching process must use multimedia. Therefore, traditional English teaching cannot be held onto traditional concepts or rejected in its entirety, as both have irreplaceable aspects.
This paper combines computer-assisted language teaching methods with English course teaching and analyzes the link design for applying computer-assisted English linguistics course teaching. Scheduling methods based on multimedia data are proposed to test the performance of cloud resource management based on reinforcement learning. Verify the role of multimedia computer-assisted English linguistics course teaching through experiments.
Use reinforcement learning to update the resource scheduling strategy and set the optimal value of hyperparameters. When the model only focuses on a certain index, it can make the index reach the optimum. For example, when
In the experimental class and the comparison class, the mean value of the percentage of students who scored 90 or more points in each dimension before the teaching experiment was 0.17. After the computer-assisted English Linguistics program, the mean value of the percentage of students who scored 90 or more points in the experimental class was increased to 0.39. Moreover, the pre- and posttests of the English academic scores showed that there was a significant difference in the English scores between the pre- and post-tests of the teaching experiment. Thus, the addition of multimedia computer-assisted teaching in the traditional English linguistics course can effectively improve students’ English achievement.