# Wireless Sensor Network and AI Application for Educational Technology Course

Authors: Xiaoyue Luo
Journal: Journal of Sensors
 (2023)

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## Abstract

Over the course of its long development, the modern educational technology curriculum has undergone several changes and amassed a lot of information. Theoretically speaking, the state places a strong priority on the use of IT in schools. Students majoring in education should take educational technology courses so that they can learn the characteristics and application techniques of core current information-based teaching media and incorporate them into their own lesson plans and classroom activities. This will help them meet the information needs of today’s classrooms as they evolve with the advent of educational modernization and availability of educational information. Thus, this research employs a wireless sensor network (WSN) to gather and send data on ed tech classes and then employs AI to assess those classes’ quality and guide real-time changes to how they are taught and complete the following tasks: (1) The development status of educational technology courses and WSN at home and abroad is introduced. (2) The application of WSN in teaching is introduced, the basic principle of GRU neural network and related optimization algorithms is expounded, and the quality evaluation system of educational technology courses is constructed. (3) The IPSO-Adam-GRU evaluation model improves the GRU neural network’s hyperparameters with the help of the improved PSO approach and Adam gradient descent. The model is fed test data for evaluation, and the findings are compared to those from an expert’s evaluation to determine how well the model performs. The results demonstrate that the model established for this article is superior to others since it provides a more accurate assessment.


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## Body

## 1. Introduction

The 21st century is a century of high technology and a century of education. The competition between economy and technology is, in the final analysis, the competition of talents and education. The modernization of a country requires the modernization of talents. Only by realizing the modernization of the quality of talents can our education be in an invincible position in the competition [1]. The modernization of talent quality is largely dependent on education, and education reform often focuses on cultivating talents with a broad range of abilities. Currently, my nation is working hard to adopt high standards of education for its citizens. An emphasis on all-round, all-inclusive education is defined by cultivating creative abilities with inventive spirit and practical competence as its primary focus. High-quality education is a contemporary educational philosophy and way of thinking that aims to develop individuals into high-quality talents capable of blending in with today’s society [2]. The core of implementing quality education is curriculum reform. How to reform the educational technology curriculum in higher normal schools on the basis of quality education is the key to ensuring the quality of educational technology personnel training. Only through courses can the primary issues with developing educational technology courses and analyzing cutting-edge educational and teaching concepts be turned into real educational power [3]. A mediator between teaching and learning is the curriculum. The curriculum is taught in order to achieve the educational concepts, facts, and knowledge that teachers wish to impart. The quality of instruction is directly impacted by the concepts of curriculum setting, scientific curriculum design, and the efficacy of curriculum in teaching [4]. Books about instructional technology now come in numerous forms and editions. The contents of various editions of textbooks are various and complete. From teaching theory to educational technology ability and from teaching resource production to teaching software operation, all include hardware operation and teaching software use. The basic knowledge of content, theory, and practice covers a wide range. With so many teaching contents, targeted learning is difficult for learners. Consequently, the creation of focused resources is a pressing issue in present education, and tailored instruction is difficult to accomplish with the existing material [5].The traditional education systems have its own advantages but have associated issues like lack of flexibility, narrow scope of programs, lack of accessibility, cost issues, and monotonous learning experience. The rapid growth of Internet and advanced technologies has ignited the need of achieving quality education anywhere, anytime online considering the choice and pace of the students. The online education system requires data transfer, which also have associated issues in terms of availability of speed and optimized network communication to ensure students can access the resources seamlessly and conveniently. There are also issues relevant to inappropriate usage of data, insecure data transfer, and breaches performed by malicious third party vendor organizations.Even in the research of educational software, the creation of courseware is fairly involved, and it is challenging for students to comprehend so many different teaching resources. The majority of the current textbooks on instructional technology are merely a collection of data points. The main problem with textbooks is the wide range of topics they cover and the variety of material they include. The learner base is relatively inconsistent, but in modern educational technology, teaching students of all majors is assumed to have the same level of learning. Despite this, learners from a variety of professional backgrounds are less likely to be consistent when it comes to their learning. Consequently, when instructing, we must accurately assess the abilities of our students, carefully craft our lesson plans to include both theoretical and practical information, and make necessary adjustments based on these considerations in order to avoid the failure of the lesson and the subsequent abandonment of the learning process halfway through [6]. Thus, achieving educational objectives requires logically organizing and adjusting the instructional content. It is crucial to adapt the curriculum and procedures when teaching with modern educational technology, finish the students’ work in accordance with that level, and assess their level of proficiency. Under typical conditions, the course’s teaching material should be integrated in accordance with the learner’s overall capacity for practical application and professional caliber. Otherwise, the teaching content is out of the scope of the learner’s ability level, and the teaching of the course will lose the value of knowledge imparting itself [7].The content of modern educational technology courses is rich and complex, involving teaching theory, hardware installation and operation, software use, selection and personalized processing of teaching materials, and production of teaching courseware. This means that the entire teaching process must consider the range of learners’ abilities to be more targeted and differentiated. The application of theory, the operation of software, and the production and processing of courseware should all be matched in content. This course uses contemporary instructional technology. It is determined by Zhao et al.’s [8] properties and operations. Wireless sensor networks (WSNs) combine computer, network, and wireless communication technologies. Tiny sensors are placed throughout the monitoring area to collect data. This self-organizing wireless multihop network may be a good alternative when wired access cannot be used to transmit data at a high enough standard [9]. This paper uses the WSN to collect and transmit the data of educational technology courses and then uses AI to evaluate the quality of educational technology courses and adjust the teaching strategies of modern educational technology courses in time.The unique contributions of the paper include the following:(i)
Exploration of the development status of education technology courses and WSN at home and abroad(ii)
Implementation of WSN, GRU neural network, and related optimization algorithm in teaching(iii)
Development of IPSO-Adam-GRU evaluation model for achieved enhanced classification resultsThe organization of the paper is as follows: Section2 discusses the review of related studies followed by the methodology adopted in Section 3. Section 4 presents the experimentation results and analysis and the conclusions are discussed in Section 5.


## 2. Related Work

To date, there are a total of 19 MOOC-related courses on major learning platforms that are part of a network resource course of contemporary educational technology courses. Students’ understanding of the course’s material is directly tied to the teaching content [10]. Massive open online courses (MOOCs) are free online courses available for any individual to enroll, thereby providing flexible and affordable means to learn new skills and technologies to progress in the professional career. The MOOCs are closely related to the needs of the industry, and it helps individual to learn skills that meet the demands of the industry following an extremely cost-effective process of learning. The courses are offered in diversified areas by professors from eminent universities. Internet connection is one of major requirements to avail such courses and could be a barrier in remote areas where connectivity is relatively low. Technical and theoretical knowledge points are the foundation of all current educational technology resource courses. When it comes to developing students’ abilities and knowledge, most courses require students to create teaching scenarios using multimedia technology, apply conventional instructional methods, collect online educational resources, create personalized teaching resources, operate and apply software, evaluate information technology, and integrate curriculum and case studies [11]. Courses that cover the installation, debugging, and installation and configuring of satellite data receiving cards, the receipt and use of IP resources, and other technical skills are also available. Knowledge of educational technology theory, teaching communication methods and theories, basic principles for selecting and implementing educational media, and fundamental principles for instructional design are all included in theoretical knowledge [12]. With the gradual promotion of educational informatization, teachers’ educational technical ability needs to closely follow the lectures of educational informatization; a growing number of academic institutions and researchers have started to place greater emphasis on the development of teachers’ educational technical skills. Normal students’ capacity to use educational technology is taught in the contemporary educational technology course. Many academics use this course as a research entry point to improve the educational technology capability for normal students, according to the course’s current teaching conditions. The key areas of curriculum innovation, teaching mode change, and teaching assessment are covered by research on modern educational technology [13]. Reference [14] redesigned the course by combining the concepts and methods of Intel’s future education and training program in the modern educational technology course and developed a problem-solving and project-based learning methodology built on cutting-edge educational technologies. Reference [15] investigates and addresses the two basic difficulties of current educational technology public courses: content setting and experimental course creation. Reference [16] examines the use of task-driven teaching approaches in order to improve the quality and efficiency of present educational technology public classes. The ultimate objective is to provide all children access to cutting-edge instructional technologies. According to reference [17], the TPACK idea was included into the design of current educational technology public courses in order to give students with a fresh viewpoint on technology integration, therefore fostering their growth of educational technology application skills. Reference [18] constructed the “FLIPPR” flipped teaching model based on the modern educational technology courses and teaching characteristics of colleges and universities, applied it in teaching practice through experimental research, and finally verified the effectiveness of the “FLIPPR” flipped teaching model constructed. Modern educational technology public courses are now being taught using a “mixed and integrated” teaching model, according to reference [19]. The actual teaching of the course on current educational technologies was then conducted using this teaching methodology. The long-used teaching method can be used to teach modern educational technology in an efficient manner. Reference [20] examines and highlights issues with present teaching practices in the sector and offers solutions by contrasting the instructional strategies used by teachers of modern educational technology courses. As a new generation of sensor network, WSN has been given a high priority by all governments. It has been used in a wide range of industries in the United States, Japan, and other nations via constant research and development [21]. Intel Corporation and the University of California, Berkeley, lead research work on “dusting” technology. They succeeded in creating a fully functional sensor the size of a bottle cap that can perform functions such as computation, detection, and communication. In 2002, Intel Research Labs researchers connected 32 sensors the size of prescription medicine bottles to the Internet to read the climate on Maine’s “Big Duck Island” to assess the conditions of a petrel’s nest [22]. In addition, Mitsubishi Electric Corporation has also successfully developed a small, low-power wireless module envisaged for WSNs, which can build a peer-to-peer network using specific low-power wireless [23]. My nation almost started researching WSNs and their modern uses at the same time as other industrialised nations. It initially made an official appearance in the information and automation field research report of the Chinese Academy of Sciences’ “Knowledge Innovation Project Pilot Field Direction Research” in 1999. As one of the five major projects in terms of civilian use, WSNs involve urban public safety, public health, safety production, intelligent transportation, environmental monitoring, and other fields [24]. The study in [25] emphasized on improving remote music education using 5G networks. The network speed was identified to have the most significant impact on students’ online classes. The framework implemented convolutional neural networks (CNNs) to train the intelligent system and also deliver remote music education to the students in the presence of 5G networks. The proposed system outperformed the traditional system and yielded an accuracy of 99.13 percent. The study in [26] used big data to upgrade the traditional system, promote coconstruction, and share digital resources and information. The study presented the concept of smart campus which focused on four spectrums, namely, student curriculum management module, information release and communication module, teaching support module, and daily office management module. The higher education management system used the WSN technology to seamlessly perform activities and ensure proper communication in the smart campus system. The study in [27] used the concept of flipped classroom in music teaching with the support of two technologies, namely, artificial intelligence and wireless networking. The teachers and students were able to interact with each other using interactive devices supported by intelligent networking technologies. The CNN module was implemented to ensure the system is smart and able to provide automatic classification of the course materials. The proposed model yielded an accuracy of 98.25% when compared with the traditional KNN system.


## 3. Method

### 3.1. Teaching Method Based on Wireless Sensor Network

The application in teaching needs to be based on the characteristics of the WSN. For example, code additions and improvements to wireless network protocols are performed. The application process includes the following: (1) analyze the course and find out the difficulties and key points that need to be used in the WSN experiment, for example, the antenna part involved in the foundation of wireless communication. Since this part of the content is very abstract and has strong theoretical knowledge, different types of antennas and performance parameters can be set through the platform in the classroom. Comparing the performance of different types of antennas enables students to intuitively understand the impact of antenna design parameters on performance. In introducing the hidden terminal and exposed terminal of the carrier sense multiple access/collision avoidance (CSMA/CA) protocol, the influence of different parameters on the performance of the protocol is reasonably designed, and then, the simulation results are analyzed by using the chart tool. CSMA/CA is a protocol that is used for carrier transmission in the 802.11 networks. It was developed with an objective to minimize the potential of collision likely to occur when multiple stations send their signals over a data link layer. The CSMA first checks the state of the medium in each station before a transmission is started. This enables averting the potential collisions by listening to the broadcasting nodes and then guiding the devices to transmit the signal if the channel is free. Hence, when a node receives a packet, it ensures the channel is clear and no other node transmits at the same time. In case the channel is not clear, the node waits for a randomly chosen time frame and then rechecks the clearance of the channel which is called the backoff factor. The channel thus remains idle while the backoff counter reaches zero and the node transmits the packet. If the channel is not free, the backoff factor is reset and the process is repeated. (2) Design the corresponding network model according to the difficulty of the course. During the simulation, the network model is established according to the main purpose to be simulated. Sensor network, sensor network communication, and sensor network service make up the three key components of WSN design. Periodic experimental data, network topology data, and other basic data types can be used to categories the sensor nodes in the system. Along with the real data of these fundamental data kinds, the data to be conveyed in the sensor network should be able to discriminate between the data of these fundamental data types. This requires adding an identification header to the header of each data packet to be transmitted to indicate which data type the data packet to be transmitted belongs to. (3) Simulate and collect data through the network model. Once the network model is built, it is necessary to collect the simulation performance and statistics of the network protocol, such as the network delay performance of a certain protocol, packet loss rate, and extra service load. Statistics can be collected on a single object in a network model, or global statistics can be collected on the entire network. For example, if you want to know whether a site is a hidden terminal, you need to collect the relevant network performance of the site, such as packet loss rate and channel collision probability. Finally, the application framework of educational technology courses based on WSNs and AI constructed in this paper is shown in Figure1.Figure 1
Framework of educational technology courses based on WSN and AI.


### 3.2. Gated Recurrent Unit

Recurrent neural networks (RNNs) are a class of neural networks that are good at dealing with sequences of nonlinear features. An LSTM and a gated recurrent unit (GRU) are the most common RNNs. LSTM, a gated RNN that can successfully tackle the gradient vanishing issue of RNN, has gained a lot of interest following GRU. Based on the LSTM neural network, GRU has been enhanced and its gating mechanism is simpler than LSTM. This greatly expedites the training process. The major advantage of using LSTM lies in its ability to address the vanishing gradient problem. The vanishing gradient problem makes network training difficult for long sequence of words or integers. The gradients are used for updating the RNN parameters, and in case of long sequence of words or integers, the gradients become smaller to an extent that no network training is possible to be performed. The LSTM networks help to eliminate such problems and enables capturing of long-term dependencies between keywords or integers in a sequence which is separated by large distance. The Hadamard product operation and sigmoid function are the GRU neural network’s fundamental operations. The Hadamard product of two vectors is similar to the concept of matrix addition. The elements corresponding to the same row and column of the given vectors or matrices are multiplied together in order to form a new vector or matrix. It is a binary operation that considers two matrices of similar dimensions and produces another matrix similar to the dimension of the operands. In the sense, each elementi, j is the product of the elements i, j of the two original matrices. The network is capable of forgetting and storing data in the range between zero and one when employing the sigmoid function. The structure diagram of GRU neural network is shown in Figure 2.Figure 2
GRU neural network structure diagram.The GRU’s reset gate, denoted in Figure2 by the sign rt, regulates how much of the past information is forgotten at once. The status of the buried layer is affected by the reduction in rt because more information from the previous instant is lost. To regulate how much of the preceding moment’s memory is kept in sync with the current state, zt acts as the GRU neural network’s update gate. The input and forgetting gates in LSTM are identical to the zt, and the bigger the zt, the more information is preserved at the present instant. Therefore, GRU neural network has higher training efficiency and less memory than LSTM.Both the reset gatert and the update gate zt are obtained by combining the input at the current moment with the state of the hidden layer at the previous moment. The reset gate output value rt and the update gate output value zt are shown in
(1)rt=σwrxt+urht−1,(2)zt=σwzxt+uzht−1.The output of the hidden layer at the previous moment is to add the reset gate output to the current inputxt and put it into the Tanh activation function to get the current activation state h~t. The output nz of the hidden layer at the previous moment and the activation state h~t of the current hidden layer are shown in
(3)nz=wxt+urt⊗ht−1,(4)h~t=Tanhnz,where ⊗ is the Hadmard product operation, W is the neural network weight of the hidden layer state at the previous moment, and u is the neural network weight when the state is input at the current moment.The advantages of GRU over LSTM are as follows:zt and 1−zt can process current and historical information at the same time, thus not only improving training speed but also saving running space. The hidden layer state h at the current moment is obtained under the combined action of the hidden layer state at the previous moment and the current hidden layer activation state, and the hidden layer state function ht at time t is shown in
(5)ht=1−ztht−1+zth~t.It can be seen from equations (1) to (5) that the GRU neural network does not increase over time and forget past information. The preceding data is instead effectively forgotten and preserved to the following GRU unit, establishing a dependence relationship between the prior moment and the present moment and preventing the gradient from dissipating.


### 3.3. Optimization Algorithm

#### 3.3.1. Particle Swarm Optimization

Particle swarm optimization (PSO) was first proposed by American academics in 1995. The PSO method is carefully examined, and the inertia weight is added to the particle speed. The PSO algorithm, also known as the standard PSO method, is improved overall by this improvement. The PSO has multiple advantages over the other traditional optimization methods. The primary advantage is its simple concept, easier implementation, robustness in the control of the parameters, and enhanced computational efficiency. The PSO yields faster and cheaper outcomes in comparison to other methods and it can also be parallelized. PSO does not use the gradient of the problem which is being optimized and it does not require problem to be differentiable. PSO may be thought of as the behavioral mechanism involved in flock foraging in birds. The area where the problem is solved is analogous to the flight space of a flock of birds, and each bird is regarded as a particle without volume and mass to represent a candidate solution to the problem. In flying space, each particle has two components of velocity and position. Firstly, the particle swarm is initialized, and the particle finds the individual extreme value by dynamically updating its own speed and position globally. At the same time, iterative search is carried out in the solution space, and the particle continuously updates the speed and position of the particle according to the optimal solution pbest found by itself and the optimal solution gbest found by the group. When two optimal solutions are found, the particle update individual velocity and position are shown in(6)vijt+1=vijt+c1r1pij−xijt+c2r2pgj−xijt,(7)xijt+1=xijt+vijt+1,where i is the number of particles in the particle swarm, i=1,2,⋯,n. j is the dimension of the target space, j=1,2,⋯,d. vi is the particle velocity, vi∈−vmax,vmax, and the size of the velocity v depends on the properties of the objective function. c1 and c2 are learning factors, also known as acceleration factors, usually c1=c2=2. r1 and r2 are uniform and mutually independent random numbers, and the value range is 0,1.The PSO algorithm’s local search capability is significantly diminished when the particle cannot effectively control its own flight speed since it is difficult for the particle to discover the ideal solution. In order to effectively control the speed of particles, the inertia weightw is introduced. The velocity and position of the improved particles are shown in
(8)vijt+1=wvijt+c1r1pij−xijt+c2r2pgj−xijt,(9)xijt+1=xijt+vijt+1.Equation (8) demonstrates that the inertia weight w has a significant impact on the particle swarm’s velocity. When w is greater, the particle step size is also greater, causing the flight speed to be faster and resulting in an overall rough search. Smaller values of w result in smaller particle step sizes, slower speeds, and finer local searches. The current position of the particle swarm is determined by the position at the previous moment and the current speed and is also indirectly affected by the inertia weight w, so the inertia weight w plays an important role in PSO.Various studies have implemented PSO in education. As an example, PSO technique was implemented in association with back propagation algorithm for feed forward neural networks. The optimization of neural network parameters was done considering parameters such as hidden neurons, learning rate, and activation function. The model was implemented on education dataset focusing on the increase in the number of private universities every year. The dataset consisted of 380 educational institutes which participated in the accreditation program of National Assessment and Accreditation Council. The hybrid of PSO and back propagation yielded promising accuracy and fitness function in comparison to the traditional state-of-the-art approaches. One study designed an optimal neural network architecture using PSO technique on the higher education data. The study used PSO technique in association with recurrent neural network LSTM to find the optimal solution for the feed forward neural network. The dataset of 500 educational institutes collected from the NAAC official site was used for the study. The hybrid model involving PSO and LSTM yielded promising results considering the RMSE and accuracy metrics.


#### 3.3.2. Adam Algorithm

Adaptive moment estimation (Adam) was proposed by scholars at the ICLR conference in 2014. There is a big difference between the Adam algorithm and the stochastic gradient descent (SGD). The SGD algorithm has only one learning rate, and the alpha function is used to generally update the learning rate in the algorithm. It is possible to change the Adam algorithm’s learning rate by adjusting the first- and second-order moment estimations of the gradient. The Adam algorithm utilizes a momentum factor and an adaptive learning rate to improve the speed of convergence. The Adam optimization algorithm has several advantages over other traditional approaches. It is easier to implement and requires lesser memory space making it computationally efficient. The algorithm works more efficiently in case of sparse gradients, nonstationary objectives, and larger datasets with larger parameters. The current time stepVt and weight θt are shown in
(10)Vt=γVt−1+η∇θJθt,(11)θt=θt−1−Vt.Adaptive adjustment is carried out in combination with different learning rates of weights, whereγ=0.9 and η=0.001 by default, and the adaptive learning rates are shown in
(12)Eg2t=0.9Eg2t−1+0.1gt2,(13)θt+1=θt−ηEg2t+ε∙gt,where Eg2t is the exponentially decaying mean of the squared gradient, gt is the gradient at the current moment, and ε is a very small parameter, in order to avoid a denominator of 0.


### 3.4. The Principle of the Improved Particle Swarm Algorithm

PSO continuously iteratively updates the solution until it finds and decides the global optimal solution. The PSO method’s iterative optimization procedure is susceptible to the problem of falling into a local optimum though. As a result, this issue must be addressed; this paper optimizes the PSO algorithm by selecting an appropriate learning factor and combining the improved inertia weight and the introduced variation factor, thereby improving the convergence speed of the PSO algorithm. The improved part of the PSO algorithm is as follows:


#### 3.4.1. Improve Inertia Weights

In this paper, a nonlinear inertia weight is used to improve the shortcomings of the standard particle swarm optimization algorithm. The improved nonlinear weightwik is shown in
(14)wik=wmax−wmax−wmintankTmax×π6.The inertia weightwik improved in this paper is a nonlinear function. When the iteration number k gradually increases, the value of the inertia weight wik gradually decreases. However, when the number of iterations k tends to infinity, the inertia weight wik will approach a fixed value, so wik first increases and then decreases to meet the global and local search requirements of the PSO algorithm. The fitness f of the particle is shown in
(15)f=1N∑i=1Nyi−y∧i2,where N is the number of samples, yi is the predicted data, and y^i is the measured data.


#### 3.4.2. Introduce a Variation Factor

This work enhances the PSO algorithm by incorporating a mutation component, which is based on the genetic algorithm’s principle of mutation. The adaptive mutation factor quickly and randomly initializes the particle with a certain mutation rate after each update of the particle’s speed and position. This increases the particle’s local search range over time, preventing the PSO algorithm from entering the local optimum. In order to accurately judge whether the PSO algorithm has fallen into the local optimal solution, the fitness variance is used as the index to judge the aggregation density during particle search. The variance is shown in(16)σ2=∑i=1Nfi−f¯F2,(17)F=max1,fi−f¯,where f¯ is the average fitness value, fi is the fitness value of the th particle, and F is the normalized calibration factor.The link between particle fitness, individual ideal fitness, global optimal fitness, and permitted error determines whether or not to mutate. The local optimum position is where the particle is trapped if it satisfies the mutation requirement. In order to make the particle jump out of this position, the position needs to be mutated and updated, so that the search range of the particle is expanded and the position mutates.


### 3.5. Educational Technology Course Quality Evaluation System

According to the theory of quality education and the principles of curriculum setting generally followed in the world, the quality assessment of educational technology courses should follow the following principles.


#### 3.5.1. The Principle of Pertinence

It means that the content of the course must be clearly targeted, that is, the target teaching object. Computer professional courses make up a significant component of the curriculum system in terms of the number of class hours. The application-oriented courses, which are closely related to the major of educational technology and include computer-aided teaching, application of multimedia technology, development of multimedia courseware, and application of network education, not only have a single course category and a small proportion of class hours, but the majority of the content also focuses on the elaboration of fundamental concepts and theories. According to the training goal of educational technology major, we need to solve the shortage of students’ information technology ability. Strengthening the development of computer courseware and hardware maintenance and the cultivation of applied talents in network education, the application-oriented talents of teaching window technology are urgently needed in the current society, which means that the future educational technology will be dominated by systematic methods and information technology.


#### 3.5.2. The Principle of Application

Learning is for application, and curriculum setting’s fundamental tenet is fit for students’ real-world application. A person’s value is revealed not just by the quantity of knowledge he has mastered but also by the quantity of accomplishments and advantages he reaps by using that knowledge. Practicality is shown in curriculum design. First, it is necessary to help students find the link between theory and practice. The second is to help students improve their ability to deal with and solve practical problems, so as to improve their personal value.


#### 3.5.3. Developmental Principle

Paying attention to the future development of students is the basic principle of quality education. Two opposing teaching philosophies can be seen in the purposes of knowledge transmission and human growth. The former is focused on encouraging people’s knowledge structures, while the latter is focused on learning. We highlight that students should be able to pursue their own independent research in the field after completing a course, allowing them to understand the teaching and learning strategies used in educational technology disciplines. Curriculum should be conducive to the cultivation of students’ various abilities, such as the ability to retrieve, process, utilize information, create thinking, learn independently, and realize self-monitoring. The courses offered by educational technology should be appropriately expanded horizontally and vertically, looking at the overall situation of educational reform and development and broadening students’ thinking.


#### 3.5.4. Scientific Principle

First of all, the setting of the curriculum must conform to the students’ cognitive regulations. In any case, the reform must respect the laws of students’ cognitive habits and psychological acceptance. Secondly, the curriculum reform should be guided by the curriculum theory, in line with the evolution and development principles of the educational technology curriculum itself.According to the above principles, the quality evaluation system of educational technology courses is constructed as shown in Table1.Table 1
Educational technology course quality evaluation system.
IndexLabelTeachers are passionate about teachingV1Teachers are full of energy and energy when teaching this courseV2Teachers can keep students interested in the classroomV3The teacher clearly explains the course contentV4Teachers will illustrate course content with examplesV5Teachers encourage students to ask questionsV6Teacher encourages students to participate in class discussionsV7Actively communicate with students and create a relaxed atmosphereV8Teachers treat each student kindlyV9Continuously update knowledge and master the frontiers of disciplinesV10Students develop a positive learning attitudeV11Looking forward to learning new knowledge and skillsV12Actively communicate with teachersV13Learned basic principles, general concepts, and theoriesV14The ability to analyze and solve problems is improvedV15


## 3.1. Teaching Method Based on Wireless Sensor Network

The application in teaching needs to be based on the characteristics of the WSN. For example, code additions and improvements to wireless network protocols are performed. The application process includes the following: (1) analyze the course and find out the difficulties and key points that need to be used in the WSN experiment, for example, the antenna part involved in the foundation of wireless communication. Since this part of the content is very abstract and has strong theoretical knowledge, different types of antennas and performance parameters can be set through the platform in the classroom. Comparing the performance of different types of antennas enables students to intuitively understand the impact of antenna design parameters on performance. In introducing the hidden terminal and exposed terminal of the carrier sense multiple access/collision avoidance (CSMA/CA) protocol, the influence of different parameters on the performance of the protocol is reasonably designed, and then, the simulation results are analyzed by using the chart tool. CSMA/CA is a protocol that is used for carrier transmission in the 802.11 networks. It was developed with an objective to minimize the potential of collision likely to occur when multiple stations send their signals over a data link layer. The CSMA first checks the state of the medium in each station before a transmission is started. This enables averting the potential collisions by listening to the broadcasting nodes and then guiding the devices to transmit the signal if the channel is free. Hence, when a node receives a packet, it ensures the channel is clear and no other node transmits at the same time. In case the channel is not clear, the node waits for a randomly chosen time frame and then rechecks the clearance of the channel which is called the backoff factor. The channel thus remains idle while the backoff counter reaches zero and the node transmits the packet. If the channel is not free, the backoff factor is reset and the process is repeated. (2) Design the corresponding network model according to the difficulty of the course. During the simulation, the network model is established according to the main purpose to be simulated. Sensor network, sensor network communication, and sensor network service make up the three key components of WSN design. Periodic experimental data, network topology data, and other basic data types can be used to categories the sensor nodes in the system. Along with the real data of these fundamental data kinds, the data to be conveyed in the sensor network should be able to discriminate between the data of these fundamental data types. This requires adding an identification header to the header of each data packet to be transmitted to indicate which data type the data packet to be transmitted belongs to. (3) Simulate and collect data through the network model. Once the network model is built, it is necessary to collect the simulation performance and statistics of the network protocol, such as the network delay performance of a certain protocol, packet loss rate, and extra service load. Statistics can be collected on a single object in a network model, or global statistics can be collected on the entire network. For example, if you want to know whether a site is a hidden terminal, you need to collect the relevant network performance of the site, such as packet loss rate and channel collision probability. Finally, the application framework of educational technology courses based on WSNs and AI constructed in this paper is shown in Figure1.Figure 1
Framework of educational technology courses based on WSN and AI.


## 3.2. Gated Recurrent Unit

Recurrent neural networks (RNNs) are a class of neural networks that are good at dealing with sequences of nonlinear features. An LSTM and a gated recurrent unit (GRU) are the most common RNNs. LSTM, a gated RNN that can successfully tackle the gradient vanishing issue of RNN, has gained a lot of interest following GRU. Based on the LSTM neural network, GRU has been enhanced and its gating mechanism is simpler than LSTM. This greatly expedites the training process. The major advantage of using LSTM lies in its ability to address the vanishing gradient problem. The vanishing gradient problem makes network training difficult for long sequence of words or integers. The gradients are used for updating the RNN parameters, and in case of long sequence of words or integers, the gradients become smaller to an extent that no network training is possible to be performed. The LSTM networks help to eliminate such problems and enables capturing of long-term dependencies between keywords or integers in a sequence which is separated by large distance. The Hadamard product operation and sigmoid function are the GRU neural network’s fundamental operations. The Hadamard product of two vectors is similar to the concept of matrix addition. The elements corresponding to the same row and column of the given vectors or matrices are multiplied together in order to form a new vector or matrix. It is a binary operation that considers two matrices of similar dimensions and produces another matrix similar to the dimension of the operands. In the sense, each elementi, j is the product of the elements i, j of the two original matrices. The network is capable of forgetting and storing data in the range between zero and one when employing the sigmoid function. The structure diagram of GRU neural network is shown in Figure 2.Figure 2
GRU neural network structure diagram.The GRU’s reset gate, denoted in Figure2 by the sign rt, regulates how much of the past information is forgotten at once. The status of the buried layer is affected by the reduction in rt because more information from the previous instant is lost. To regulate how much of the preceding moment’s memory is kept in sync with the current state, zt acts as the GRU neural network’s update gate. The input and forgetting gates in LSTM are identical to the zt, and the bigger the zt, the more information is preserved at the present instant. Therefore, GRU neural network has higher training efficiency and less memory than LSTM.Both the reset gatert and the update gate zt are obtained by combining the input at the current moment with the state of the hidden layer at the previous moment. The reset gate output value rt and the update gate output value zt are shown in
(1)rt=σwrxt+urht−1,(2)zt=σwzxt+uzht−1.The output of the hidden layer at the previous moment is to add the reset gate output to the current inputxt and put it into the Tanh activation function to get the current activation state h~t. The output nz of the hidden layer at the previous moment and the activation state h~t of the current hidden layer are shown in
(3)nz=wxt+urt⊗ht−1,(4)h~t=Tanhnz,where ⊗ is the Hadmard product operation, W is the neural network weight of the hidden layer state at the previous moment, and u is the neural network weight when the state is input at the current moment.The advantages of GRU over LSTM are as follows:zt and 1−zt can process current and historical information at the same time, thus not only improving training speed but also saving running space. The hidden layer state h at the current moment is obtained under the combined action of the hidden layer state at the previous moment and the current hidden layer activation state, and the hidden layer state function ht at time t is shown in
(5)ht=1−ztht−1+zth~t.It can be seen from equations (1) to (5) that the GRU neural network does not increase over time and forget past information. The preceding data is instead effectively forgotten and preserved to the following GRU unit, establishing a dependence relationship between the prior moment and the present moment and preventing the gradient from dissipating.


## 3.3. Optimization Algorithm

### 3.3.1. Particle Swarm Optimization

Particle swarm optimization (PSO) was first proposed by American academics in 1995. The PSO method is carefully examined, and the inertia weight is added to the particle speed. The PSO algorithm, also known as the standard PSO method, is improved overall by this improvement. The PSO has multiple advantages over the other traditional optimization methods. The primary advantage is its simple concept, easier implementation, robustness in the control of the parameters, and enhanced computational efficiency. The PSO yields faster and cheaper outcomes in comparison to other methods and it can also be parallelized. PSO does not use the gradient of the problem which is being optimized and it does not require problem to be differentiable. PSO may be thought of as the behavioral mechanism involved in flock foraging in birds. The area where the problem is solved is analogous to the flight space of a flock of birds, and each bird is regarded as a particle without volume and mass to represent a candidate solution to the problem. In flying space, each particle has two components of velocity and position. Firstly, the particle swarm is initialized, and the particle finds the individual extreme value by dynamically updating its own speed and position globally. At the same time, iterative search is carried out in the solution space, and the particle continuously updates the speed and position of the particle according to the optimal solution pbest found by itself and the optimal solution gbest found by the group. When two optimal solutions are found, the particle update individual velocity and position are shown in(6)vijt+1=vijt+c1r1pij−xijt+c2r2pgj−xijt,(7)xijt+1=xijt+vijt+1,where i is the number of particles in the particle swarm, i=1,2,⋯,n. j is the dimension of the target space, j=1,2,⋯,d. vi is the particle velocity, vi∈−vmax,vmax, and the size of the velocity v depends on the properties of the objective function. c1 and c2 are learning factors, also known as acceleration factors, usually c1=c2=2. r1 and r2 are uniform and mutually independent random numbers, and the value range is 0,1.The PSO algorithm’s local search capability is significantly diminished when the particle cannot effectively control its own flight speed since it is difficult for the particle to discover the ideal solution. In order to effectively control the speed of particles, the inertia weightw is introduced. The velocity and position of the improved particles are shown in
(8)vijt+1=wvijt+c1r1pij−xijt+c2r2pgj−xijt,(9)xijt+1=xijt+vijt+1.Equation (8) demonstrates that the inertia weight w has a significant impact on the particle swarm’s velocity. When w is greater, the particle step size is also greater, causing the flight speed to be faster and resulting in an overall rough search. Smaller values of w result in smaller particle step sizes, slower speeds, and finer local searches. The current position of the particle swarm is determined by the position at the previous moment and the current speed and is also indirectly affected by the inertia weight w, so the inertia weight w plays an important role in PSO.Various studies have implemented PSO in education. As an example, PSO technique was implemented in association with back propagation algorithm for feed forward neural networks. The optimization of neural network parameters was done considering parameters such as hidden neurons, learning rate, and activation function. The model was implemented on education dataset focusing on the increase in the number of private universities every year. The dataset consisted of 380 educational institutes which participated in the accreditation program of National Assessment and Accreditation Council. The hybrid of PSO and back propagation yielded promising accuracy and fitness function in comparison to the traditional state-of-the-art approaches. One study designed an optimal neural network architecture using PSO technique on the higher education data. The study used PSO technique in association with recurrent neural network LSTM to find the optimal solution for the feed forward neural network. The dataset of 500 educational institutes collected from the NAAC official site was used for the study. The hybrid model involving PSO and LSTM yielded promising results considering the RMSE and accuracy metrics.


### 3.3.2. Adam Algorithm

Adaptive moment estimation (Adam) was proposed by scholars at the ICLR conference in 2014. There is a big difference between the Adam algorithm and the stochastic gradient descent (SGD). The SGD algorithm has only one learning rate, and the alpha function is used to generally update the learning rate in the algorithm. It is possible to change the Adam algorithm’s learning rate by adjusting the first- and second-order moment estimations of the gradient. The Adam algorithm utilizes a momentum factor and an adaptive learning rate to improve the speed of convergence. The Adam optimization algorithm has several advantages over other traditional approaches. It is easier to implement and requires lesser memory space making it computationally efficient. The algorithm works more efficiently in case of sparse gradients, nonstationary objectives, and larger datasets with larger parameters. The current time stepVt and weight θt are shown in
(10)Vt=γVt−1+η∇θJθt,(11)θt=θt−1−Vt.Adaptive adjustment is carried out in combination with different learning rates of weights, whereγ=0.9 and η=0.001 by default, and the adaptive learning rates are shown in
(12)Eg2t=0.9Eg2t−1+0.1gt2,(13)θt+1=θt−ηEg2t+ε∙gt,where Eg2t is the exponentially decaying mean of the squared gradient, gt is the gradient at the current moment, and ε is a very small parameter, in order to avoid a denominator of 0.


## 3.3.1. Particle Swarm Optimization

Particle swarm optimization (PSO) was first proposed by American academics in 1995. The PSO method is carefully examined, and the inertia weight is added to the particle speed. The PSO algorithm, also known as the standard PSO method, is improved overall by this improvement. The PSO has multiple advantages over the other traditional optimization methods. The primary advantage is its simple concept, easier implementation, robustness in the control of the parameters, and enhanced computational efficiency. The PSO yields faster and cheaper outcomes in comparison to other methods and it can also be parallelized. PSO does not use the gradient of the problem which is being optimized and it does not require problem to be differentiable. PSO may be thought of as the behavioral mechanism involved in flock foraging in birds. The area where the problem is solved is analogous to the flight space of a flock of birds, and each bird is regarded as a particle without volume and mass to represent a candidate solution to the problem. In flying space, each particle has two components of velocity and position. Firstly, the particle swarm is initialized, and the particle finds the individual extreme value by dynamically updating its own speed and position globally. At the same time, iterative search is carried out in the solution space, and the particle continuously updates the speed and position of the particle according to the optimal solution pbest found by itself and the optimal solution gbest found by the group. When two optimal solutions are found, the particle update individual velocity and position are shown in(6)vijt+1=vijt+c1r1pij−xijt+c2r2pgj−xijt,(7)xijt+1=xijt+vijt+1,where i is the number of particles in the particle swarm, i=1,2,⋯,n. j is the dimension of the target space, j=1,2,⋯,d. vi is the particle velocity, vi∈−vmax,vmax, and the size of the velocity v depends on the properties of the objective function. c1 and c2 are learning factors, also known as acceleration factors, usually c1=c2=2. r1 and r2 are uniform and mutually independent random numbers, and the value range is 0,1.The PSO algorithm’s local search capability is significantly diminished when the particle cannot effectively control its own flight speed since it is difficult for the particle to discover the ideal solution. In order to effectively control the speed of particles, the inertia weightw is introduced. The velocity and position of the improved particles are shown in
(8)vijt+1=wvijt+c1r1pij−xijt+c2r2pgj−xijt,(9)xijt+1=xijt+vijt+1.Equation (8) demonstrates that the inertia weight w has a significant impact on the particle swarm’s velocity. When w is greater, the particle step size is also greater, causing the flight speed to be faster and resulting in an overall rough search. Smaller values of w result in smaller particle step sizes, slower speeds, and finer local searches. The current position of the particle swarm is determined by the position at the previous moment and the current speed and is also indirectly affected by the inertia weight w, so the inertia weight w plays an important role in PSO.Various studies have implemented PSO in education. As an example, PSO technique was implemented in association with back propagation algorithm for feed forward neural networks. The optimization of neural network parameters was done considering parameters such as hidden neurons, learning rate, and activation function. The model was implemented on education dataset focusing on the increase in the number of private universities every year. The dataset consisted of 380 educational institutes which participated in the accreditation program of National Assessment and Accreditation Council. The hybrid of PSO and back propagation yielded promising accuracy and fitness function in comparison to the traditional state-of-the-art approaches. One study designed an optimal neural network architecture using PSO technique on the higher education data. The study used PSO technique in association with recurrent neural network LSTM to find the optimal solution for the feed forward neural network. The dataset of 500 educational institutes collected from the NAAC official site was used for the study. The hybrid model involving PSO and LSTM yielded promising results considering the RMSE and accuracy metrics.


## 3.3.2. Adam Algorithm

Adaptive moment estimation (Adam) was proposed by scholars at the ICLR conference in 2014. There is a big difference between the Adam algorithm and the stochastic gradient descent (SGD). The SGD algorithm has only one learning rate, and the alpha function is used to generally update the learning rate in the algorithm. It is possible to change the Adam algorithm’s learning rate by adjusting the first- and second-order moment estimations of the gradient. The Adam algorithm utilizes a momentum factor and an adaptive learning rate to improve the speed of convergence. The Adam optimization algorithm has several advantages over other traditional approaches. It is easier to implement and requires lesser memory space making it computationally efficient. The algorithm works more efficiently in case of sparse gradients, nonstationary objectives, and larger datasets with larger parameters. The current time stepVt and weight θt are shown in
(10)Vt=γVt−1+η∇θJθt,(11)θt=θt−1−Vt.Adaptive adjustment is carried out in combination with different learning rates of weights, whereγ=0.9 and η=0.001 by default, and the adaptive learning rates are shown in
(12)Eg2t=0.9Eg2t−1+0.1gt2,(13)θt+1=θt−ηEg2t+ε∙gt,where Eg2t is the exponentially decaying mean of the squared gradient, gt is the gradient at the current moment, and ε is a very small parameter, in order to avoid a denominator of 0.


## 3.4. The Principle of the Improved Particle Swarm Algorithm

PSO continuously iteratively updates the solution until it finds and decides the global optimal solution. The PSO method’s iterative optimization procedure is susceptible to the problem of falling into a local optimum though. As a result, this issue must be addressed; this paper optimizes the PSO algorithm by selecting an appropriate learning factor and combining the improved inertia weight and the introduced variation factor, thereby improving the convergence speed of the PSO algorithm. The improved part of the PSO algorithm is as follows:


### 3.4.1. Improve Inertia Weights

In this paper, a nonlinear inertia weight is used to improve the shortcomings of the standard particle swarm optimization algorithm. The improved nonlinear weightwik is shown in
(14)wik=wmax−wmax−wmintankTmax×π6.The inertia weightwik improved in this paper is a nonlinear function. When the iteration number k gradually increases, the value of the inertia weight wik gradually decreases. However, when the number of iterations k tends to infinity, the inertia weight wik will approach a fixed value, so wik first increases and then decreases to meet the global and local search requirements of the PSO algorithm. The fitness f of the particle is shown in
(15)f=1N∑i=1Nyi−y∧i2,where N is the number of samples, yi is the predicted data, and y^i is the measured data.


### 3.4.2. Introduce a Variation Factor

This work enhances the PSO algorithm by incorporating a mutation component, which is based on the genetic algorithm’s principle of mutation. The adaptive mutation factor quickly and randomly initializes the particle with a certain mutation rate after each update of the particle’s speed and position. This increases the particle’s local search range over time, preventing the PSO algorithm from entering the local optimum. In order to accurately judge whether the PSO algorithm has fallen into the local optimal solution, the fitness variance is used as the index to judge the aggregation density during particle search. The variance is shown in(16)σ2=∑i=1Nfi−f¯F2,(17)F=max1,fi−f¯,where f¯ is the average fitness value, fi is the fitness value of the th particle, and F is the normalized calibration factor.The link between particle fitness, individual ideal fitness, global optimal fitness, and permitted error determines whether or not to mutate. The local optimum position is where the particle is trapped if it satisfies the mutation requirement. In order to make the particle jump out of this position, the position needs to be mutated and updated, so that the search range of the particle is expanded and the position mutates.


## 3.4.1. Improve Inertia Weights

In this paper, a nonlinear inertia weight is used to improve the shortcomings of the standard particle swarm optimization algorithm. The improved nonlinear weightwik is shown in
(14)wik=wmax−wmax−wmintankTmax×π6.The inertia weightwik improved in this paper is a nonlinear function. When the iteration number k gradually increases, the value of the inertia weight wik gradually decreases. However, when the number of iterations k tends to infinity, the inertia weight wik will approach a fixed value, so wik first increases and then decreases to meet the global and local search requirements of the PSO algorithm. The fitness f of the particle is shown in
(15)f=1N∑i=1Nyi−y∧i2,where N is the number of samples, yi is the predicted data, and y^i is the measured data.


## 3.4.2. Introduce a Variation Factor

This work enhances the PSO algorithm by incorporating a mutation component, which is based on the genetic algorithm’s principle of mutation. The adaptive mutation factor quickly and randomly initializes the particle with a certain mutation rate after each update of the particle’s speed and position. This increases the particle’s local search range over time, preventing the PSO algorithm from entering the local optimum. In order to accurately judge whether the PSO algorithm has fallen into the local optimal solution, the fitness variance is used as the index to judge the aggregation density during particle search. The variance is shown in(16)σ2=∑i=1Nfi−f¯F2,(17)F=max1,fi−f¯,where f¯ is the average fitness value, fi is the fitness value of the th particle, and F is the normalized calibration factor.The link between particle fitness, individual ideal fitness, global optimal fitness, and permitted error determines whether or not to mutate. The local optimum position is where the particle is trapped if it satisfies the mutation requirement. In order to make the particle jump out of this position, the position needs to be mutated and updated, so that the search range of the particle is expanded and the position mutates.


## 3.5. Educational Technology Course Quality Evaluation System

According to the theory of quality education and the principles of curriculum setting generally followed in the world, the quality assessment of educational technology courses should follow the following principles.


### 3.5.1. The Principle of Pertinence

It means that the content of the course must be clearly targeted, that is, the target teaching object. Computer professional courses make up a significant component of the curriculum system in terms of the number of class hours. The application-oriented courses, which are closely related to the major of educational technology and include computer-aided teaching, application of multimedia technology, development of multimedia courseware, and application of network education, not only have a single course category and a small proportion of class hours, but the majority of the content also focuses on the elaboration of fundamental concepts and theories. According to the training goal of educational technology major, we need to solve the shortage of students’ information technology ability. Strengthening the development of computer courseware and hardware maintenance and the cultivation of applied talents in network education, the application-oriented talents of teaching window technology are urgently needed in the current society, which means that the future educational technology will be dominated by systematic methods and information technology.


### 3.5.2. The Principle of Application

Learning is for application, and curriculum setting’s fundamental tenet is fit for students’ real-world application. A person’s value is revealed not just by the quantity of knowledge he has mastered but also by the quantity of accomplishments and advantages he reaps by using that knowledge. Practicality is shown in curriculum design. First, it is necessary to help students find the link between theory and practice. The second is to help students improve their ability to deal with and solve practical problems, so as to improve their personal value.


### 3.5.3. Developmental Principle

Paying attention to the future development of students is the basic principle of quality education. Two opposing teaching philosophies can be seen in the purposes of knowledge transmission and human growth. The former is focused on encouraging people’s knowledge structures, while the latter is focused on learning. We highlight that students should be able to pursue their own independent research in the field after completing a course, allowing them to understand the teaching and learning strategies used in educational technology disciplines. Curriculum should be conducive to the cultivation of students’ various abilities, such as the ability to retrieve, process, utilize information, create thinking, learn independently, and realize self-monitoring. The courses offered by educational technology should be appropriately expanded horizontally and vertically, looking at the overall situation of educational reform and development and broadening students’ thinking.


### 3.5.4. Scientific Principle

First of all, the setting of the curriculum must conform to the students’ cognitive regulations. In any case, the reform must respect the laws of students’ cognitive habits and psychological acceptance. Secondly, the curriculum reform should be guided by the curriculum theory, in line with the evolution and development principles of the educational technology curriculum itself.According to the above principles, the quality evaluation system of educational technology courses is constructed as shown in Table1.Table 1
Educational technology course quality evaluation system.
IndexLabelTeachers are passionate about teachingV1Teachers are full of energy and energy when teaching this courseV2Teachers can keep students interested in the classroomV3The teacher clearly explains the course contentV4Teachers will illustrate course content with examplesV5Teachers encourage students to ask questionsV6Teacher encourages students to participate in class discussionsV7Actively communicate with students and create a relaxed atmosphereV8Teachers treat each student kindlyV9Continuously update knowledge and master the frontiers of disciplinesV10Students develop a positive learning attitudeV11Looking forward to learning new knowledge and skillsV12Actively communicate with teachersV13Learned basic principles, general concepts, and theoriesV14The ability to analyze and solve problems is improvedV15


## 3.5.1. The Principle of Pertinence

It means that the content of the course must be clearly targeted, that is, the target teaching object. Computer professional courses make up a significant component of the curriculum system in terms of the number of class hours. The application-oriented courses, which are closely related to the major of educational technology and include computer-aided teaching, application of multimedia technology, development of multimedia courseware, and application of network education, not only have a single course category and a small proportion of class hours, but the majority of the content also focuses on the elaboration of fundamental concepts and theories. According to the training goal of educational technology major, we need to solve the shortage of students’ information technology ability. Strengthening the development of computer courseware and hardware maintenance and the cultivation of applied talents in network education, the application-oriented talents of teaching window technology are urgently needed in the current society, which means that the future educational technology will be dominated by systematic methods and information technology.


## 3.5.2. The Principle of Application

Learning is for application, and curriculum setting’s fundamental tenet is fit for students’ real-world application. A person’s value is revealed not just by the quantity of knowledge he has mastered but also by the quantity of accomplishments and advantages he reaps by using that knowledge. Practicality is shown in curriculum design. First, it is necessary to help students find the link between theory and practice. The second is to help students improve their ability to deal with and solve practical problems, so as to improve their personal value.


## 3.5.3. Developmental Principle

Paying attention to the future development of students is the basic principle of quality education. Two opposing teaching philosophies can be seen in the purposes of knowledge transmission and human growth. The former is focused on encouraging people’s knowledge structures, while the latter is focused on learning. We highlight that students should be able to pursue their own independent research in the field after completing a course, allowing them to understand the teaching and learning strategies used in educational technology disciplines. Curriculum should be conducive to the cultivation of students’ various abilities, such as the ability to retrieve, process, utilize information, create thinking, learn independently, and realize self-monitoring. The courses offered by educational technology should be appropriately expanded horizontally and vertically, looking at the overall situation of educational reform and development and broadening students’ thinking.


## 3.5.4. Scientific Principle

First of all, the setting of the curriculum must conform to the students’ cognitive regulations. In any case, the reform must respect the laws of students’ cognitive habits and psychological acceptance. Secondly, the curriculum reform should be guided by the curriculum theory, in line with the evolution and development principles of the educational technology curriculum itself.According to the above principles, the quality evaluation system of educational technology courses is constructed as shown in Table1.Table 1
Educational technology course quality evaluation system.
IndexLabelTeachers are passionate about teachingV1Teachers are full of energy and energy when teaching this courseV2Teachers can keep students interested in the classroomV3The teacher clearly explains the course contentV4Teachers will illustrate course content with examplesV5Teachers encourage students to ask questionsV6Teacher encourages students to participate in class discussionsV7Actively communicate with students and create a relaxed atmosphereV8Teachers treat each student kindlyV9Continuously update knowledge and master the frontiers of disciplinesV10Students develop a positive learning attitudeV11Looking forward to learning new knowledge and skillsV12Actively communicate with teachersV13Learned basic principles, general concepts, and theoriesV14The ability to analyze and solve problems is improvedV15


## 4. Experiment and Analysis

### 4.1. Experimental Data and Preprocessing

This work builds an experimental dataset of 635 sets of data in accordance with the educational technology course quality evaluation system. In order to make the trained prediction model more accurate and ensure that the unit indicators of different types of data are in the same order of magnitude, this paper uses a normalization method to process the experimental data, the purpose of which is to map the experimental data to the [0, 1] interval. Normalization is shown in(18)y=x−xminxmax−xmin.


### 4.2. IPSO-Adam-GRU Prediction Model Parameter Settings

The biggest disadvantage of GRU neural network is that it is difficult to reasonably determine the hyperparameters, which is easy to cause overfitting, which reduces the accuracy of prediction. In view of the above shortcomings, this paper uses the IPSO algorithm for global optimization and reduces the training range to the neighborhood of the global optimal solution, thereby shortening the running time and improving the training accuracy. Since the hyperparameters may be determined adaptively during the training process thanks to the local optimization carried out by the Adam algorithm, there is less chance that the model’s prediction accuracy will suffer from inappropriate hyperparameter selection. The hyperparameters optimized in this paper include the number of hidden layers, the number of neurons in each hidden layer, and the learning rate.


#### 4.2.1. The Choice of the Number of Hidden Layers

Reasonable selection of the number of hidden layers of GRU neural network is the first step in optimizing GRU neural network. IPSO optimizes one hidden layer of GRU, and IPSO optimizes two hidden layers of GRU as IPS0-GRU1 and IPS0-GRU2, respectively. Similarly, the optimization of one hidden layer of GRU by PSO and the optimization of two hidden layers of GRU by PSO are expressed as PSO-GRU1 and PSO-GRU2, respectively. Comparing the fitness of the above four models, the fitness comparison between IPSO-GRU1 and IPSO-GRU2 is shown in Figure3. The fitness comparison between PSO-GRU1 and PSO-GRU2 is shown in Figure 4.Figure 3
Adaptability comparison of IPSO-GRU1 and IPSO-GRU2.Figure 4
Adaptability comparison of PSO-GRU1 and PSO-GRU2.It can be seen from the horizontal comparison of Figures3 and 4 that the fitness values of IPSO-GRU1 and IPSO-GRU2 are much lower than those of PSO-GRU1 and PSO-GRU2. That is to say, regardless of whether the hidden layer is one layer or two layers, the optimization accuracy of IPSO-GRU is much higher than that of PS0-GRU. It has been confirmed that IPSO, which was suggested in Chapter 3, has a higher optimization accuracy than PSO. As the number of iterations rises, PSO-GRU optimizes twice before the fitness tends to a fixed value. However, IPSO-GRU is chosen since it can attempt optimization more quickly and avoid hitting the local optimum. The fitness of IPSO-GRU1 and IPSO-GRU2 can be compared longitudinally in Figure 3, which demonstrates that when the GRU neural network has two hidden layers, the optimization accuracy is higher than that of only one hidden layer. Although the accuracy is not much improved, considering that GRU belongs to deep learning, it is generally a multilayer hidden layer, and because the data predicted in this paper is nonlinear. In order to achieve high prediction accuracy of the model, this paper selects two hidden layers.


#### 4.2.2. Selection of the Number of Neurons in the Hidden Layer of Each Layer

GRU neural networks with two hidden layers and IPSO and Adam gradient descent algorithms tuned and chosen for hyperparameters have a greater optimization accuracy than those with only one hidden layer. The number of neurons in the first hidden layer, the number of neurons in the second hidden layer, and the learning rate are all tuned hyperparameters. The number of iterations has been adjusted to 50 to make tracking easier. As illustrated in Figures5 and 6, the first hidden layer optimization and second hidden layer optimization are both displayed in the same figure.Figure 5
Optimization of the number of neurons in the first hidden layer.Figure 6
Optimization of the number of neurons in the second hidden layer.As can be seen in Figure5, there are increasingly less neurons in the first hidden layer, and this number remains an integer throughout the optimization process. When there are between 0 and 20 iterations, the first hidden layer can have up to 23 neurons. The number of neurons is optimized more quickly as the number of iterations rises. Hyperparameter optimization is shown to be faster and more efficient using IPSO. To get the appropriate number of neurons in the first hidden layer, we may iterate up to 22 times, which is what we call a constant value. Because of this, the first hidden layer has 8 neurons.Figure6 shows that the number of neurons in the second hidden layer is increasing during the optimization process, and that number is an integer. There must be at least 10 neurons in the second hidden layer when the number of iterations is between zero and twenty. As the number of iterations increases, the number of neurons gets optimized more quickly. The local optimum is avoided in IPSO as the number of neurons increases. The number of neurons in the second hidden layer tends to stabilize at 12, which is a constant amount, when the number of repeats approaches 22. As a result, there are now just 12 neurons in the second hidden layer.


#### 4.2.3. Learning Rate Optimization

The learning rate is an important hyperparameter in the GRU neural network. The learning rate is mainly used to adjust the network weight by adjusting the gradient length in the Adam gradient descent algorithm. The smaller the learning rate used, the higher the optimization accuracy and the easier it is to converge to a local minimum. The higher the learning rate, the lower the local optimization accuracy and easy to miss local minima. The learning rate optimization is shown in Figure7.Figure 7
Learning rate optimization.It can be seen from Figure7 that the whole process of learning rate optimization is on the rise, but the learning rate value range is constant. When the number of iterations is in [0, 20], the learning rate is the smallest and kept at 0.665×10−2. As the number of iterations increases, the learning rate optimization speed increases rapidly. When the number of iterations increases to 21, the learning rate optimization tends to a constant value, which is 0.962×10−2.


### 4.3. Optimal Model Performance Detection

The model is initialized with the experiment’s best parameters, and after being trained with the training set of data, the best model is discovered. The model is then fed the data from the test set, and the output is compared to the output from the experts. The final experimental results are shown in Table2. It can be seen that the model proposed in this paper is very close to the expert evaluation results, which proves the performance superiority of the model.Table 2
Comparison of model output results with expert evaluation results.
Number12345678Model output0.7560.8190.8850.9120.6270.6750.7330.849Expert results0.7490.8180.8850.9080.6280.6740.7310.850


## 4.1. Experimental Data and Preprocessing

This work builds an experimental dataset of 635 sets of data in accordance with the educational technology course quality evaluation system. In order to make the trained prediction model more accurate and ensure that the unit indicators of different types of data are in the same order of magnitude, this paper uses a normalization method to process the experimental data, the purpose of which is to map the experimental data to the [0, 1] interval. Normalization is shown in(18)y=x−xminxmax−xmin.


## 4.2. IPSO-Adam-GRU Prediction Model Parameter Settings

The biggest disadvantage of GRU neural network is that it is difficult to reasonably determine the hyperparameters, which is easy to cause overfitting, which reduces the accuracy of prediction. In view of the above shortcomings, this paper uses the IPSO algorithm for global optimization and reduces the training range to the neighborhood of the global optimal solution, thereby shortening the running time and improving the training accuracy. Since the hyperparameters may be determined adaptively during the training process thanks to the local optimization carried out by the Adam algorithm, there is less chance that the model’s prediction accuracy will suffer from inappropriate hyperparameter selection. The hyperparameters optimized in this paper include the number of hidden layers, the number of neurons in each hidden layer, and the learning rate.


### 4.2.1. The Choice of the Number of Hidden Layers

Reasonable selection of the number of hidden layers of GRU neural network is the first step in optimizing GRU neural network. IPSO optimizes one hidden layer of GRU, and IPSO optimizes two hidden layers of GRU as IPS0-GRU1 and IPS0-GRU2, respectively. Similarly, the optimization of one hidden layer of GRU by PSO and the optimization of two hidden layers of GRU by PSO are expressed as PSO-GRU1 and PSO-GRU2, respectively. Comparing the fitness of the above four models, the fitness comparison between IPSO-GRU1 and IPSO-GRU2 is shown in Figure3. The fitness comparison between PSO-GRU1 and PSO-GRU2 is shown in Figure 4.Figure 3
Adaptability comparison of IPSO-GRU1 and IPSO-GRU2.Figure 4
Adaptability comparison of PSO-GRU1 and PSO-GRU2.It can be seen from the horizontal comparison of Figures3 and 4 that the fitness values of IPSO-GRU1 and IPSO-GRU2 are much lower than those of PSO-GRU1 and PSO-GRU2. That is to say, regardless of whether the hidden layer is one layer or two layers, the optimization accuracy of IPSO-GRU is much higher than that of PS0-GRU. It has been confirmed that IPSO, which was suggested in Chapter 3, has a higher optimization accuracy than PSO. As the number of iterations rises, PSO-GRU optimizes twice before the fitness tends to a fixed value. However, IPSO-GRU is chosen since it can attempt optimization more quickly and avoid hitting the local optimum. The fitness of IPSO-GRU1 and IPSO-GRU2 can be compared longitudinally in Figure 3, which demonstrates that when the GRU neural network has two hidden layers, the optimization accuracy is higher than that of only one hidden layer. Although the accuracy is not much improved, considering that GRU belongs to deep learning, it is generally a multilayer hidden layer, and because the data predicted in this paper is nonlinear. In order to achieve high prediction accuracy of the model, this paper selects two hidden layers.


### 4.2.2. Selection of the Number of Neurons in the Hidden Layer of Each Layer

GRU neural networks with two hidden layers and IPSO and Adam gradient descent algorithms tuned and chosen for hyperparameters have a greater optimization accuracy than those with only one hidden layer. The number of neurons in the first hidden layer, the number of neurons in the second hidden layer, and the learning rate are all tuned hyperparameters. The number of iterations has been adjusted to 50 to make tracking easier. As illustrated in Figures5 and 6, the first hidden layer optimization and second hidden layer optimization are both displayed in the same figure.Figure 5
Optimization of the number of neurons in the first hidden layer.Figure 6
Optimization of the number of neurons in the second hidden layer.As can be seen in Figure5, there are increasingly less neurons in the first hidden layer, and this number remains an integer throughout the optimization process. When there are between 0 and 20 iterations, the first hidden layer can have up to 23 neurons. The number of neurons is optimized more quickly as the number of iterations rises. Hyperparameter optimization is shown to be faster and more efficient using IPSO. To get the appropriate number of neurons in the first hidden layer, we may iterate up to 22 times, which is what we call a constant value. Because of this, the first hidden layer has 8 neurons.Figure6 shows that the number of neurons in the second hidden layer is increasing during the optimization process, and that number is an integer. There must be at least 10 neurons in the second hidden layer when the number of iterations is between zero and twenty. As the number of iterations increases, the number of neurons gets optimized more quickly. The local optimum is avoided in IPSO as the number of neurons increases. The number of neurons in the second hidden layer tends to stabilize at 12, which is a constant amount, when the number of repeats approaches 22. As a result, there are now just 12 neurons in the second hidden layer.


### 4.2.3. Learning Rate Optimization

The learning rate is an important hyperparameter in the GRU neural network. The learning rate is mainly used to adjust the network weight by adjusting the gradient length in the Adam gradient descent algorithm. The smaller the learning rate used, the higher the optimization accuracy and the easier it is to converge to a local minimum. The higher the learning rate, the lower the local optimization accuracy and easy to miss local minima. The learning rate optimization is shown in Figure7.Figure 7
Learning rate optimization.It can be seen from Figure7 that the whole process of learning rate optimization is on the rise, but the learning rate value range is constant. When the number of iterations is in [0, 20], the learning rate is the smallest and kept at 0.665×10−2. As the number of iterations increases, the learning rate optimization speed increases rapidly. When the number of iterations increases to 21, the learning rate optimization tends to a constant value, which is 0.962×10−2.


## 4.2.1. The Choice of the Number of Hidden Layers

Reasonable selection of the number of hidden layers of GRU neural network is the first step in optimizing GRU neural network. IPSO optimizes one hidden layer of GRU, and IPSO optimizes two hidden layers of GRU as IPS0-GRU1 and IPS0-GRU2, respectively. Similarly, the optimization of one hidden layer of GRU by PSO and the optimization of two hidden layers of GRU by PSO are expressed as PSO-GRU1 and PSO-GRU2, respectively. Comparing the fitness of the above four models, the fitness comparison between IPSO-GRU1 and IPSO-GRU2 is shown in Figure3. The fitness comparison between PSO-GRU1 and PSO-GRU2 is shown in Figure 4.Figure 3
Adaptability comparison of IPSO-GRU1 and IPSO-GRU2.Figure 4
Adaptability comparison of PSO-GRU1 and PSO-GRU2.It can be seen from the horizontal comparison of Figures3 and 4 that the fitness values of IPSO-GRU1 and IPSO-GRU2 are much lower than those of PSO-GRU1 and PSO-GRU2. That is to say, regardless of whether the hidden layer is one layer or two layers, the optimization accuracy of IPSO-GRU is much higher than that of PS0-GRU. It has been confirmed that IPSO, which was suggested in Chapter 3, has a higher optimization accuracy than PSO. As the number of iterations rises, PSO-GRU optimizes twice before the fitness tends to a fixed value. However, IPSO-GRU is chosen since it can attempt optimization more quickly and avoid hitting the local optimum. The fitness of IPSO-GRU1 and IPSO-GRU2 can be compared longitudinally in Figure 3, which demonstrates that when the GRU neural network has two hidden layers, the optimization accuracy is higher than that of only one hidden layer. Although the accuracy is not much improved, considering that GRU belongs to deep learning, it is generally a multilayer hidden layer, and because the data predicted in this paper is nonlinear. In order to achieve high prediction accuracy of the model, this paper selects two hidden layers.


## 4.2.2. Selection of the Number of Neurons in the Hidden Layer of Each Layer

GRU neural networks with two hidden layers and IPSO and Adam gradient descent algorithms tuned and chosen for hyperparameters have a greater optimization accuracy than those with only one hidden layer. The number of neurons in the first hidden layer, the number of neurons in the second hidden layer, and the learning rate are all tuned hyperparameters. The number of iterations has been adjusted to 50 to make tracking easier. As illustrated in Figures5 and 6, the first hidden layer optimization and second hidden layer optimization are both displayed in the same figure.Figure 5
Optimization of the number of neurons in the first hidden layer.Figure 6
Optimization of the number of neurons in the second hidden layer.As can be seen in Figure5, there are increasingly less neurons in the first hidden layer, and this number remains an integer throughout the optimization process. When there are between 0 and 20 iterations, the first hidden layer can have up to 23 neurons. The number of neurons is optimized more quickly as the number of iterations rises. Hyperparameter optimization is shown to be faster and more efficient using IPSO. To get the appropriate number of neurons in the first hidden layer, we may iterate up to 22 times, which is what we call a constant value. Because of this, the first hidden layer has 8 neurons.Figure6 shows that the number of neurons in the second hidden layer is increasing during the optimization process, and that number is an integer. There must be at least 10 neurons in the second hidden layer when the number of iterations is between zero and twenty. As the number of iterations increases, the number of neurons gets optimized more quickly. The local optimum is avoided in IPSO as the number of neurons increases. The number of neurons in the second hidden layer tends to stabilize at 12, which is a constant amount, when the number of repeats approaches 22. As a result, there are now just 12 neurons in the second hidden layer.


## 4.2.3. Learning Rate Optimization

The learning rate is an important hyperparameter in the GRU neural network. The learning rate is mainly used to adjust the network weight by adjusting the gradient length in the Adam gradient descent algorithm. The smaller the learning rate used, the higher the optimization accuracy and the easier it is to converge to a local minimum. The higher the learning rate, the lower the local optimization accuracy and easy to miss local minima. The learning rate optimization is shown in Figure7.Figure 7
Learning rate optimization.It can be seen from Figure7 that the whole process of learning rate optimization is on the rise, but the learning rate value range is constant. When the number of iterations is in [0, 20], the learning rate is the smallest and kept at 0.665×10−2. As the number of iterations increases, the learning rate optimization speed increases rapidly. When the number of iterations increases to 21, the learning rate optimization tends to a constant value, which is 0.962×10−2.


## 4.3. Optimal Model Performance Detection

The model is initialized with the experiment’s best parameters, and after being trained with the training set of data, the best model is discovered. The model is then fed the data from the test set, and the output is compared to the output from the experts. The final experimental results are shown in Table2. It can be seen that the model proposed in this paper is very close to the expert evaluation results, which proves the performance superiority of the model.Table 2
Comparison of model output results with expert evaluation results.
Number12345678Model output0.7560.8190.8850.9120.6270.6750.7330.849Expert results0.7490.8180.8850.9080.6280.6740.7310.850


## 5. Conclusion

In order to develop teachers who can master contemporary educational theories, flexibly use a variety of contemporary teaching media and means to integrate teaching resources, and support the efficient growth of educational informatization, higher education institutions offer a public course on educational technology. Its curriculum content is broad, applicable, and practical and is made up of various academic fields like education, computer science, psychology, learning, and science. As for the educational technology public course materials currently on the market, from the perspective of teaching content, it consists of three parts: basic education theory, basic information technology, and cases of information technology and curriculum integration. From the syllabus of the course, generally 30% of the class time is used to teach teaching concepts, 30% of the class time is used to teach modern teaching techniques, and 30% of the class time is used to conduct case analysis. To sum up, the educational technology course includes the knowledge, practice, activity content structure, and practice space for the smooth development of innovative education, and such a comprehensive course is very conducive to the development of innovative education. Modern information technology is used by teachers to virtualize actual teaching situations, create an immersive learning environment, pique students’ interest in learning, pique their cognitive conflict, and develop students’ problem-solving skills. Encourage children to learn on their own, to see the connections between what they are learning and their peers, teachers, and the world at large, and to finally understand how to apply and transfer what they have learned. Therefore, this paper uses the wireless sensor network to collect and transmit the data of educational technology courses and then uses artificial intelligence to evaluate the quality of educational technology courses, adjust the teaching strategies of modern educational technology courses in time, and complete the following tasks: (1) The development status of educational technology courses and WSN at home and abroad is introduced. (2) The application of WSN in teaching is introduced, the basic principle of GRU neural network and related optimization algorithms is expounded, and the quality evaluation system of educational technology courses is constructed. (3) The improved PSO algorithm and Adam gradient descent are used to optimize the hyperparameters of the GRU neural network, thereby constructing the IPSO-Adam-GRU evaluation model. The test data is input into the model for performance testing, and the output results are compared with the expert evaluation results. The results show that the evaluation accuracy of the model in this paper is very high, which further prove the superiority of the proposed model. The study model could be further evaluated on larger dataset and compared with the traditional state-of-the-art algorithm considering additional metrics of evaluation. This could be considered for implementation as part of the future research work.



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*Source: 2093354-2023-02-10.xml*