# Construction of College Chinese Mobile Learning Environment Based on Intelligent Reinforcement Learning Technology in Wireless Network Environment

Authors: Jiao Zhao
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Technological advancements have resulted in the implementation of technology in various fields. Technologies such as wireless network systems and artificial intelligence have opened new doors in education. The Chinese language reflects the rich tradition of the greatest civilization in the world. Therefore, the university courses in a language with excellent traditional and cultural values have to be updated concerning technological growth. This research article will study the construction of a Chinese university language learning mobile environment based on intelligent reinforcement learning technology in a wireless network environment. Reinforcement learning technology (RLT) is a machine learning training technique that provides rewards and penalties. It is a decision-making approach in which artificial intelligence will face a game-like situation. Artificial intelligence either get rewarded or penalized for its actions. This learning method will significantly increase the transmission level between the student and teacher communication. Still, identifying whether the children are attentive while listening to their online classes is one of the hardest things. Once the energy loss has been rectified, it is possible to improve the learning platform in all universities, colleges, and other educational platforms using cloud computing technologies. With the implementation of RLT, the students and the teachers will be rewarded when there is an increased performance for the Chinese learning system. The results have been recorded at a low packet rate with reduced wireless communication technology. When the learning mechanism is compared to the Q-learning (quality learning) mechanism, a 98.78% accuracy rate can be seen.


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## Body

## 1. Introduction

Despite the numerous benefits of mobile learning (M-learning), there is a paucity of research about the distinctive behaviors or learning characteristics of M-learners, which has hindered the efficiency of M-learning. Additional complications arise from the widespread usage of mobile phones in e-learning, U-learning, and M-learning contexts, which makes it difficult to provide adaptive learning information [1]. Because mobile devices have a limited amount of screen real estate, instructional materials cannot be presented in the same way that they are on desktop computers or internet applications. Because of the poor and unpredictable internet connections experienced by mobile learners, it is difficult for them to access educational materials. The use of mobile learning over traditional and web-based methods of training has a number of advantages [2]. Most youngsters in regular classrooms are not as capable of making rapid adjustments as children who have been exposed to M-learning. Mobile phone technology makes it possible for “M-learners” (mobile learners) to access and study educational resources at any time and from any location, regardless of their physical location. Students can choose their own course materials in M-learning, just as they can in regular classrooms, rather than having course materials prescribed to them by an instructor [3]. Computers are used as part of an adaptive learning system to facilitate interactions between students and to provide students with individualized learning materials that are tailored to their specific requirements and development. Adaptive learning (AL) systems change their content to meet the individual needs of each learner based on their reactions, interactions, and feedback from other learners. This technology is not only useful in the classroom but also can be used for professional development and training in the workplace, which is advantageous. Since the inception of adaptive learning (AL) systems, desktop and web-based applications have been the primary focus of research and development activities [4]. The provision of personalized learning content for an adaptive learning system is a significant challenge. Early intervention requires a complete grasp of each student’s learning style and preferences and their strengths and weaknesses. When it comes to delivering teaching to students in the most effective manner possible, adaptive learning systems can take advantage of a range of different qualities [5]. If the relevant learning characteristics are present, machine learning (ML) and deep learning (DL) algorithms can be utilized to mimic learning behavior. If a machine learning or deep learning algorithm is correctly trained, it can be used to detect factors that have a significant impact on students’ performance on standardized tests. There are algorithms called ML/DL multiclass classification algorithms that can be used to put students into groups based on their interactions and performances [6].When an ILS and machine learning methodologies are combined, an M-learning model can be created that accurately reflects the unique learning behavior of each learner. ML models that are properly constructed can be used to effectively predict and identify students’ learning characteristics such as strengths, limitations, preferences, and performance [7]. Various machine learning techniques, such as support vector machines (SVM), decision trees (DTs), random forests (RF), and artificial neural networks, were employed in the development of this machine learning model (ANNs) [8]. The process of developing an M-learning model involves first identifying appropriate learning features (such as time and location, preferences, and performance), followed by the creation of a dataset of learning characteristics, the analysis of those features, and the preprocessing of those features, followed by the selection of the appropriate ML algorithm and the training, testing, and evaluation of the mode. Once the model is completed, the process is repeated again and again [9]. Using the M-learning approach, ILS will be able to incorporate adaptive guidance and intelligent guiding into its systems. The M-learning paradigm, for example, can provide additional reading materials or allow the student to continue with the course. As fresh information is gathered by M-learners, adaptive M-learning systems can change and update the model used to create the M-learning system [10]. In M-learning systems, misinformation abounds, making it impossible to make sense of it all at the same time. Machine learning algorithms can be used to represent individual learners’ learning behavior in a learning environment. These algorithms can express sophisticated feature connections, feature weights, and how these feature combinations affect the learning behaviors of individual learners.Deep learning (DL) allows models to hone in on their most significant qualities without the need for any human intervention or supervision [11]. AI scientists have now achieved their long-term goal of constructing computers that can demonstrate intelligence in the same way that humans do: they have created computers that are capable of deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), self-organizing maps (SOMs), and artificial neural networks (ANNs), which are among the most widely used [12]. A dearth of studies exists on how various M-learners interpret and value the numerous factors that comprise their distinct learning styles and preferences when it comes to mobile learning, and this is particularly true in the case of online learning (M-learning). Additional complications arise from the widespread usage of mobile phones in e-learning, U-learning, and M-learning contexts, which makes it difficult to provide adaptive learning information [13]. Because mobile devices have a limited amount of screen real estate, instructional materials cannot be presented in the same way that they are on desktop computers or internet applications. Because of the poor and unpredictable internet connections experienced by mobile learners, it is difficult for them to access educational materials [14]. The use of mobile learning over traditional and web-based methods of training has a number of advantages. Most youngsters in regular classrooms are not as capable of making rapid adjustments as children who have been exposed to M-learning. Mobile phone technology makes it possible for “M-learners” (mobile learners) to access and study educational resources at any time and from any location, regardless of their physical location [15]. Students can choose their own course materials in M-learning, just as they can in regular classrooms, rather than having course materials prescribed to them by an instructor. Computers are used as part of an adaptive learning system to facilitate interactions between students and to provide students with individualized learning materials that are tailored to their specific requirements and development [16]. When producing learning content for AL systems, the specific requirements of each learner are taken into consideration. This technology is not only useful in the classroom; it can also be used for professional development and training in the workplace, which is advantageous [17]. Since the beginning of adaptive learning (AL) systems, the main focus of research and development has been on desktop and web-based applications.The provision of personalized learning content for an adaptive learning system is a significant challenge [18]. Early intervention requires a complete grasp of each student’s learning style and preferences and their strengths and weaknesses [19]. When it comes to delivering teaching to students in the most effective manner possible, adaptive learning systems can take advantage of a range of different qualities. If the relevant learning characteristics are present, machine learning (ML) and deep learning (DL) algorithms can be utilized to mimic learning behavior [20]. If a machine learning or deep learning algorithm is correctly trained, it can be used to detect factors that have a significant impact on students’ performance on standardized tests. Students can be classified into performance groups based on their previous interactions and performances using ML/DL multiclass classification algorithms, which are based on their previous interactions and performances [21]. When an ILS and machine learning methodologies are combined, an M-learning model can be created that accurately reflects the unique learning behavior of each learner [22]. ML models that are properly constructed can be used to effectively predict and identify students’ learning characteristics such as strengths, limitations, preferences, and performance. Various machine learning techniques, such as support vector machines (SVM), decision trees (DTs), random forests (RF), and artificial neural networks, were employed in the development of this machine learning model (ANNs) [23]. The process of developing an M-learning model involves first identifying appropriate learning features (such as time and location, preferences, and performance), followed by the creation of a dataset of learning characteristics, the analysis of those features, and the preprocessing of those features, followed by the selection of the appropriate ML algorithm and the training, testing, and evaluation of the model [24]. Once the model is completed, the process is repeated again and again. Using the M-learning approach, ILS will be able to incorporate adaptive guidance and intelligent guiding into its systems. The M-learning paradigm, for example, can provide additional reading materials or allow the student to continue with the course [25]. The researcher focused on optimizing the teaching mode of higher education. The teaching mode of colleges and the learning situation of college students are studied based on mobile learning. The artificial neural network (ANN) algorithm is implemented to train and test the student responses [26]. This study focused on constructing Chinese language learning through mobile environment based on intelligent reinforcement learning technology in a wireless network environment.The contributions of the work are as follows:(i)
The study proposed multimobile learning arithmetic for featuring mobile learning(ii)
It focused on solving the problem of how agent selects a moving occasion and minimize the computational costs(iii)
The reinforcement for Chinese language learning through mobile technology’s goal is for mobile agents to study through probability-changing environments autonomously and concurrently


### 1.1. Motivation for the Study

The Chinese language is rich in tradition, and it also projects the culture of the greatest civilization in the world. Chinese literature was written thousands of years ago during the period of various Chinese dynasties. The Chinese language has its own literary tradition, with the record of written literature dating back to the ancient days. The proposed system is aimed at achieving university Chinese language learning through a mobile environment. The progress of Chinese language learning through mobile technology is discussed using reinforcement learning technology. It will then be enlarged to include mobile learning. Finally, a multimobile learning arithmetic (MML) approach is proposed. The feature of mobile learning is fully taken into account by MML. It can force an agent to make decisions in an uncertain situation with a conflict target. It can also solve the problem of how an agent chooses a moving occasion and hugely reduce the computational cost. The reinforcement for Chinese language learning through mobile technology’s goal is for mobile agents to study through probability-changing environments autonomously and concurrently. According to simulation results, the suggested processing is predicated on reinforcement learning, and grouping is a fast and efficient technique.


## 1.1. Motivation for the Study

The Chinese language is rich in tradition, and it also projects the culture of the greatest civilization in the world. Chinese literature was written thousands of years ago during the period of various Chinese dynasties. The Chinese language has its own literary tradition, with the record of written literature dating back to the ancient days. The proposed system is aimed at achieving university Chinese language learning through a mobile environment. The progress of Chinese language learning through mobile technology is discussed using reinforcement learning technology. It will then be enlarged to include mobile learning. Finally, a multimobile learning arithmetic (MML) approach is proposed. The feature of mobile learning is fully taken into account by MML. It can force an agent to make decisions in an uncertain situation with a conflict target. It can also solve the problem of how an agent chooses a moving occasion and hugely reduce the computational cost. The reinforcement for Chinese language learning through mobile technology’s goal is for mobile agents to study through probability-changing environments autonomously and concurrently. According to simulation results, the suggested processing is predicated on reinforcement learning, and grouping is a fast and efficient technique.


## 2. Materials and Methods

“Knowledge of languages is the doorway to wisdom.” A famous English proverb establishes the importance of language. Language is not only a mere communication tool. It also represents the country’s culture, arts, literature, cultural knowledge, etc. Learning a language includes speaking, listening, reading, and writing. Most people believe that language is simply a communication tool we use in our daily lives. The fact is that language is more than a communication tool. In this lifetime, one cannot neglect a language because the inner thinking, i.e., the words that the human mind speaks or thinks inside itself, is done with the help of a language. Learning Chinese helps to explore the excellent culture and traditional sentiments, enhancing the ability of ideologies to enrich our lives. The Chinese language reflects the rich tradition of the greatest civilization in the world. Chinese literature was written thousands of years ago during the rule of various Chinese dynasties. Therefore, the Chinese language is a literary tradition with ancient written literature records.People are getting into the new technological era, which means many data are collected and stored within a database. Here, the data is gathered to make predictions. Even a robot should have a sensor in it. Only then could the robots sense the things around them and find the right solution to recover them. At the same time, technological developments have introduced a lot of hardware and software projects that reduce the human effort required to monitor something. When a device should be made through wireless connections, it should be part of the involvement of intelligent technologies. Within the past 15 years, people or people in business have gained unique social media, web applications, etc. If there are no more social media publications globally, it will affect business people and marketing. Before social media presence, if a company wanted to advertise their products, they would suggest television advertisements or banners. When many people got into the social media world, their work became more manageable. As a result, the number of customers gained by the sellers has improved in a short period with wireless technology. The education system has been made better by its use and technology.While having the reports of development for the past few years, it is identified that energy consumption is one of the critical problems to be rectified. Technological development is impossible without the level of energy consumption. In recent days, most people would understand the value of mobility in managing problems. The Internet of Things is a growing technology where every device can be connected, increasing the number of connections for communication between electronic and wireless devices. Already the education system has been developed, and it is being updated too. For example, children use mobile phones, laptops, and tablets as IoT devices.One of the most significant reasons is that IoT-connected devices make everything online on smartphones and laptops and in cars, medical management devices, and sometimes farming equipment. In most universities, they provide a wearable device for the students so that the management can monitor their activities at any time. Using these wearable devices, teachers could identify the student’s presence in the school or online platforms. Through this kind of device, having attendance during the class would be unnecessary. While using such concepts related to cloud computing technology, Moodle and a few networking platforms are advantages. With the help of this technology, it is always possible to make mobile learning more sufficient and easy to access. Virtual is the concept of connecting the students and the teacher when living in a different place. Some of the online platforms provide a free course that can be accessed by the student at any time he wishes. Here, cloud technology plays a significant part in completing the process. Figure1 represents the basic model of mobile learning. Analyzing the initial stage is one of the essential concepts under mobile learning. The distinction is that the emphasis is on mobile learning activities to eventually create an environment for such mobile learning activities that helps relate to cloud-based hardware and software technology. Ecological management and humanistic cloud management release a few advanced levels of mobile learning activity.Figure 1
Learning Chinese language through mobile environment (WSN).Using mobile technology, a self-learning procedure on Chinese learning has been proposed to solve adaptive motion planning on mobile robots in unstructured environments. First, a learning framework was created for the responsive motion planning of such a mobile manipulator in unstructured environments using sensor data, and a mathematics framework for every element of the learning algorithm was suggested. Then, using inductive reasoning, the generalization problem of a consistent state space of the reinforcement learning system was solved, the size was reduced, and the learning algorithm’s frequency continued to increase. Ultimately, mobile learning achieved a responsive navigation system through identity after simulating the self-learning methodology based on the learning method in an environment with various obstacles.In this proposed system, the Chinese language is taught using mobile environment. All the course selection, study plan, resource management, and additional information are represented in Figure1. Teaching and learning process of the language through mobile learning can be performed through the mobile, laptops, tablets, and so on. Reinforcement learning technology (RTL) is implemented which focuses on the awarding the persons performing teaching and learning processes. This will improve the individual’s performance that will provide encouragement for taking the Chinese language learning course by the students. The findings of the study attest to the reinforcement method’s efficacy steps.
(1)
The sampling rate is 128, the number of iterations is 5, the number of training is 10, and the number of training is 5(2)
Video/audio input quantification 28 28(3)
Loading the dataset’s audio/video input(4)
Varying exploration:X is the test set of data (15000, 28, 28, 1), and k is the train set of data (90000, 28, 28, 1)(5)
Design but also compile designs(6)
Network trainingThe preceding algorithm explains the general steps involved in training and testing the Chinese language learning classification dataset in ANN. Individual actions could be detected in real time, making sure the accuracy and timeliness of notifications. As a result, behavioral science acknowledgement has implications for theory, and it became a focus of research in a wide range of fields. Once images can also be recognized as images or time-based, recognition system becomes a classification task.ANN is considered datasetDs and its containing s objects a1¸a2¸⋯..as1ai (1≤i≤s), FRj is defined as attribute, Y is the variables, and R is the probability of Ri¸Rj.The reinforcement learning technology outlines the overall steps in training and validation the image recognition set of statistics following Equation (1).Reinforcement learning technology considersDs to be a dataset with s object classes a1¸a2¸⋯..as1ai1≤i≤s.FRj is described as attribute ascribe, Y is parameters, and R is the possibility of Ri¸Rj.(1)FRj=−∑v∈domainRjYRj=hlogYRj=h,Once images can also be recognized as images or time-based, the recognition system becomes a classification task which represent(2)LRi¸R=FRi+FRj−FRi¸R,(3)1≤i¸j≤ni≠j¸n represents attributes and x is objects of a1,a2,⋯,an, where ai=ai1,ai2⋯,ain, differential of a0 defined as
(4)h^ao=∑i=1nTxdilogx−xylogy−aTad+a∑i=1nFRao¸i,Tx is significant to receive information on Chinese language learning through mobile technology di environment and reinforcement learning technology personality automatically from large amounts of visual information, and also to analyze mobile learning actions following Equation (6).
(5)WXyi=21−11+exp−Hxyi,(6)Tdi=21−11+exp−Fadi.Despite the fact that science and technology are advancing at fast speeds and data transmission quantities are skyrocketing, the requirement to extract behavioral science data from enormous video datasets has become an essential issue in a range of disciplines using Equation (7). When using intelligent surveillance cameras, the footage may be designed and analyzed in real time.
(7)Tad=∑i=1nTadiFadi.The key advantage is thatTadi is a data framework, given that performance management is genuine. The most frequent lossy abilities compression framework used today, ANN of the two-dimensional in academic training/teaching handling, is used in Equations (8) and (9), the most common lossy abilities compression framework used today.
(8)Fao¸i=0,ifnao¸i=1δ,&nao¸i¸otherwise.Individualδ activities might be identified in real time, ensuring notification accuracy and timeliness.
(9)δa=a−1loga−1−aloga.As a result, behavioral science acceptance has ramifications for theory, and it has become a subject of Equation (10) study in a variety of domains.
(10)h^ao=∑i=1nlogx−xylogy−xFxy+x∑i=1nFao¸i.Equations (11) and (12) create a Dsa function that ensures by having to add random qualities to two coefficients. The bigger the value of x, the stronger the force. Implantation and extraction method are described using
(11)Dsa=Ds−DsminDsmax−Dsmin,(12)Dsa=Ds−Dsmeanσd−xFxy+x∑i=1nFao¸i.The abovexFxy mentioned Chinese language learning through mobile technology is that if Equation (13) does not truly ignore the Dsa audio/video, the correlations cannot be implemented to the desired link without causing harm to the data included in the specified link.
(13)dmin=∫Tx∙F+R+x∑i=1nFao¸i.The primary investigation found that Chinese language learning through mobile technology classes was a good trade-off fordmin duration: long enough to recognize gadget types but short enough to be completely loaded up with some intriguing multipacks from dmin. If dmin does not contain enough outstanding packages to fill dmin, protection with 0 qualities is used to calculate Equation (14) size is standouts.
(14)Ycopy=Ydmin=1F=expTx·F+R1+expTx·F+R.We suggested a threeYclass-strategy that is adaptable but also applicable to a rising number of device kinds. First, in Equation (15), we train a single classifier for each device type. Each classifier provides a paired choice indicating whether or not the input unique mark impression belongs to the device category.
(15)logitYclass=logYclass1−Yclass=Tx·F+R.A few classifiers can detect an Equation (16) conceal unique mark perception and therefore coordinate a few device types. In such cases, l logitYclass is utilized to cast the deciding vote amongst distinct matches using an alter separation-based metric. While change separation can be employed on its own to identify device types, it requires longer effort than ordering separation.
(16)dmin=1,logitYclass>0.5,0,logitYclass<0.5.The lesser the difference, more the effectively the Chinese language learning through mobile technology resource’s given skill points challengerandomjEijnr the learner’s knowledge marks after
(17)Finr=∑j∈LrandomjFijnDs,u,T;R,ɸ the spending optimization approach with both instruction programs represents the whole expenditure information between educational techniques defined as
(18)u,T;R,ɸ=d−0.6∫−∞+∞dτhτ−re−jbτdτThe primary Equation (19) The difference in chinese language learning through mobile technology time necessary to finish educational materials is clarified by the education timeframe Lsu objectives. Learning delay with FiRiu=YRRu.
(19)Lsu=∑i=1nFiRiu=YRRuThe provisional licence total controller design performance also andɸs,n the learning path made more by comments section function through reconfiguring coefficient values, as shown by Equation (20), which would be a functional excellent illustration of the customized learning navigational optimization method.
(20)ɸs,n=Rs,n2δ2expsRs,n∗F3δ2∗eiRs,n∗Y−e−δ23It includes ten thousand data records under the data amount of each data record, with the stipulated restriction of great learning quantification but rather decision-making dissemination. Once T is between 1.7 and 1.9, the variability of the random variables used in evaluation and decision-making is flat. The defined amount of analysis but also decision-making dispersion is as low as 7% for each analysis but rather the decision-making estimation error. The Euclidian scattering of various grades of higher learning assets under data quantity 300,400,700,900 data’s defined level of 12.25%, 15.61%, 21.72%, and 27.23% higher education energy evaluation and decision-making dispersion are combined within a category. The classification could also improve the effectiveness and also increase the accuracy of the 1500 data points’ specified level of 39.41% higher education research study rather than decision-making through the data analysis. Hence, minimizing errors in higher education research and collecting data and indeed decision-making are needed.


## 3. Results and Discussion

Figure2 depicts not only evaluation but also the judgment precision of higher education resource development in the context of information processing.Figure 2
Performance analysis for learning Chinese in mobile environment based on intelligent reinforcement learning technology.Reinforcement learning technology considersDs to be a dataset with s object classes a1¸a2¸⋯,as1ai1≤i≤s, FRj described as ascribe, V is the parameters, and P is the possibility of Ri¸Rj. This equation is used and representing Figure 2. When applied to Chinese language learning through mobile technology, the mobile learning class evaluation time(s) and position on the matter method obtained from data extraction will also efficiently carry out intellectual concurrent evaluation and consequence, but it would also convert big data identification and selection across all learning practices into a gadget for user notification.As a result of the large data analysis and indeed choice evaluation of the student, educational teacher leaders can identify inadequacies in students’ knowledge, and individualized teaching is chose to bring out for schools based on the vulnerabilities of student teachers’ knowledge, which also improves its educational level. Table1 represents analysis for the relationship between data volume of Chinese language learning through mobile technology.Table 1
Result analysis for the relationship between data volume of learning and evaluation of environment based reinforcement learning technology.
Learning Chinese via mobile environment data quantity (thousand)Mobile learning evaluation and decision time (s)0.290.4120.6180.819124The key advantage is thatTadi is a data framework, given that performance management is genuine. Based on Equation (9), Figure 3 represents the relationship between both higher education evaluation and selection proportion of total and disc storage and the removal of information. Once there seems to be an enhanced number of scientific methods, they seem to have little effect on the collection surface, demonstrating that such a higher education analysis and position on the matter techniques under information processing is also organized and financially viable.Figure 3
Performance analysis of Chinese language learning classes of reinforcement learning technology of evaluations and decisions.To analyze the Chinese language learning through mobile technology with reinforcement learning technology, the evaluations and decisions have been considered. When implemented on a mobile earning system, a data exploration and development education evaluation and also decision-making technique will also carry out smart similar decision-making process. It will turn big data size evaluation among with the selection to all educational methods into a tool for user notification (refer to Table2).Table 2
Analyze the result information analysis of online learning for higher education.
The Chinese language learning classes based on reinforcement learning technology of evaluations and decisionsAccuracy (%)Chinese language learning through mobile technology11.96Environmental base learning14.74Reinforcement learning22.56Online learning classes25.62Mobile based higher education35.18To be completely loaded up with some intriguing multipacks fromdmin. If dmin does not contain enough outstanding packages to fill dmin, protection with 0 qualities is used to calculate Equation (14) size is standouts. Based on this retrieval, Figure 4 clearly illustrates not only how the accuracy of university education measurement but also selection evolves over time under data mining. The longer the time span, the more accurate the higher learning measurement but also selection, which has already been ensured to the highest level possible. It is also the most efficient in higher education information analysis and decision-making techniques. The numerical value and the percentage analysis of the Chinese language learning through mobile technology are given in Table 3. The analysis of learning Chinese language through mobile environment with the implementation of reinforcement technology has achieved an accuracy of 98.78%.Figure 4
Performance analysis of Chinese language learning on the optimization of teaching using wireless network environment.Table 3
Learning Chinese language performance result analysis on the optimization of teaching for higher education using a wireless network environment.
ParametersClassificationCountPercentage (%)Watching mobile learning in live classMale46588.65Female68386.57Residential online environmentTraining45749.87Testing58788.23Child interacting with instructorUniversity53298.78College and school9926.94Reinforcement learning technologyExtra-curricular activities68791.43Overall Chinese language learning based on reinforcement learning technology accuracy98.78u,T;R,ɸ the spending optimization approach with both instruction programs represents the whole expenditure information between educational techniques defined as Equation (18). Figure 5 shows the results of a search for Chinese language learning in a residential area learning technology for higher education optimization. Students believe that the current face-to-face method is the superior way to carry out the entire teaching-learning activities and that the framework should be used as a complement to enhance the educational process, despite the challenges they encountered. As a result, 68.52% of students’ desire expressions of teaching and learning, 52.37% favor a mix of traditional and virtual classes, and also 18.63% favor online-based educational opportunities.Figure 5
Chinese language learning on residential environment learning technology the optimization higher education.When implemented to a Chinese language learning through the mobile technology platform, its higher education analysis and stance technique obtained through the data analysis. It can perform intelligent concurrent identification and selection, and generate massive results evidently and also select among all learning practices into a method for user confirmation (refer Table4).Table 4
Result Chinese language learning on residential environment learning technology the optimization higher education.
ParametersTraining/testingMeanS. D.Mean differenceStd. error differenceAccuracy (%)Watching mobile learning in live class29.64.152.26-0.490.1893.38Residential online environment35.43.172.47-0.840.1895.53Child interacting with instructor38.73.633.730.060.3897.86Reinforcement learning technology39.33.963.18-0.430.3999.72Big data measurement and stance analysis in the classroom can identify the weak points in students’ knowledge, and highly personalized education is chosen to transmit to students based on the points in their knowledge, which improves the teaching quality even further. The existing method in Chinese language learning through mobile technology classes provides the testing (85.54%) and testing (89.02%) and overall accuracy (95, 67%). The proposed model has provided the result for Chinese language learning through mobile technology class based on training (89.56%) and testing (91.23) and overall accuracy (98.78%) (refer to Table5).Table 5
Comparison result analysis for the existing method.
AlgorithmLearning Chinese language through mobile environment—trainingLearning Chinese language through mobile—testingAccuracyReinforcement learning technology89.5691.2398.78Q-learning85.3489.0295.67


## 4. Conclusions

In this study, reinforcement learning is used to examine the development of learning the Chinese language through a mobile environment. A mobile learning environment has the potential to compel an agent to make snap decisions in the face of conflict with a potentially hostile target. An agent’s choice of a moving occasion can also be solved, and the computational cost is greatly reduced by this method. The goal of reinforcement learning technology is to allow mobile agents to study in constantly changing environments independently and simultaneously. In this study, the reinforcement algorithm is compared with Q-learning. The study results proved that reinforcement learning technology has obtained an accuracy of 98.78%.



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