# Stable Parallel Algorithms for Interdisciplinary Computer-Based Online Education with Real Problem Scenarios for STEM Education

Authors: Liangfu Jiang; Haoran Yuan
Journal: Complexity
 (2021)

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## Abstract

In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdisciplinary knowledge points related to the theme are determined. Based on the project theme, the teaching design model of the STEM-based robotics project was constructed. The analysis of the results showed that the STEM-based middle-school robotics project not only increased students’ interest in robotics but also promoted their creativity, problem-solving, teamwork, and communication skills, as well as their learning of other subjects. In this paper, an adaptive overflow-aware loss amplification strategy is proposed, which effectively alleviates the training nonconvergence problem caused by gradient overflow in mixed accuracy training. Also, this paper demonstrates that data-parallel training under multiple machines and multiple cards should use local batch normalization, which is particularly effective in accelerating neural networks containing more batch normalization layers.


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## Body

## 1. Introduction

STEM education is a relatively new teaching approach and teaching concept, which takes the enhancement of students’ STEM literacy as the core task and differs from traditional subdisciplinary knowledge education in that STEM education is an integrated inquiry education [1]. It is emphasized that disciplinary education should focus more on the connections between disciplines. Educational practices in various countries show that STEM education helps to develop students’ essential knowledge skills including innovative consciousness, critical thinking, and problem-solving ability. Robotics education itself is a multidisciplinary field, and the emphasis on breaking down disciplinary independence and integrating disciplinary knowledge in STEM education is exactly in line with robotics teaching [2]. By using STEM education concepts to guide the development of robotics education, we can not only integrate it with disciplinary knowledge but also provide a new way of thinking about robotics teaching. The most widely used curriculum model in elementary and secondary schools is the compartmentalized model, which lacks interconnectedness between disciplines, and STEM education provides a curriculum design that integrates multiple disciplines to make up for this deficiency. The classroom activities in traditional education are teacher-cantered full-time classes and face-to-face instruction [3]. This teaching method allows for more systematic teaching, more efficient learning for students, a good learning environment, and adequate communication and exchange with classmates. Moreover, although traditional education is based on books, it is not only about knowledge but also about the correct outlook on life, values, and worldview [1]. The teacher plays a leading role in the learning process, and when the students deviate, the teacher can find and correct them in time, and the teacher-student relationship in learning is irreplaceable in other ways.With the introduction of robotics education and some teaching practices, how to better carry out a robotics curriculum has become a hot issue for academic research. This study integrates STEM education concepts into robotics education, combines project design methods, selects robotics teaching project themes for specific learning groups in a targeted way, carries out robotics teaching project planning and design, constructs a teaching design model for robotics projects, and finally carries out teaching design and practice discussions of robotics teaching, as well as in the process of teaching practice. It is an important practice to explore the application of STEM education concept in robotics teaching, and it has certain reference and guiding significance for the design of robotics projects in primary and secondary schools and the subsequent promotion of robotics teaching practice research in primary and secondary schools [4]. Of course, there are many successful cases of combining mobile education with the Internet, and a large number of online education platforms provide curriculum education and VIP tutoring services through the Internet. But this has not yet involved the ability of AI, personalized education in the education platform has a lot of landing points, and this thesis discusses one of the very basic aspects, that is, the personalized course recommendation ability [5]. As a promising technical direction in the context of big data, this thesis hopes to combine it with the course recommendation system based on the mobile online education platform and use the technical capability of graph computing itself to support the characteristics of massive data to cope with the problem of too large data scale of an online education platform. In a nutshell, it is to explore the algorithm design and implementation of graph computing technology in the course recommendation system on a mobile education platform.Therefore, this study uses the STEM education concept as a guide, adopts the project-based teaching method based on the existing robotics teaching platform according to students’ developmental characteristics, designs a robotics project theme for junior high school, builds a teaching design model for the robotics project on this basis, completes the teaching design of the project theme at the same time, and tests the effects of project-based activities under the STEM education concept on junior high-school-level learners’ knowledge and problem-solving, creativity, and teamwork skills through implementation and application. The project-based activities will be used to test the effects of STEM education on learners’ knowledge of robotics and problem-solving, creativity, and teamwork skills at the middle-school level and to explore the effects on learners’ attitudes toward learning. We can recommend suitable courses to users by analysing user-related data and gradually improve users’ demand information and favourite preferences through continuous accurate recommendations and then paint a user portrait and develop different learning programs based on different user portraits to meet the personalization of user learning. Therefore, the research on personalized recommendation algorithms is meaningful.


### 1.1. Status of Research

Vehlken proposed the “STEM Education Program” in the United States as an educational program that encourages students to major in science, technology, engineering, and mathematics [6]. An et al. proposed a network combining the advantages of CNN and Factorisation-machine-supported Neural Networks (FNN) to solve the shortcomings of traditional recommendation algorithms by using auxiliary information such as text to provide additional information, which effectively solved the problem of data sparsity [7]. Yu et al. proposed a sparse edge noise reduction automatic coding model to extract product features from product review texts by combining user reviews with ratings, and, at the same time, the target user may be influenced by neighbouring users by adding this influence element into the model and proposed a recommended method of sparse edge noise reduction automatic coding by adding the influence factor of neighbouring users [8]. The experimental results show a significant improvement compared with the traditional model. Haubeck et al. proposed a recommendation hybrid model combining collaborative filtering and content based on the academic search and data mining platform Amine for a personalized recommendation to users [9]. They introduced a word vector model by incorporating deep learning methods, mapping users and papers to a word vector space, and using Word Mover Distance (WMD) to calculate similarity [10]. In recent years, more and more scholars have been devoted to the study and understanding of complex networks, and the most popular research is on bipartite graphs [11]. The core feature of bipartite graphs is that all nodes in a complex network can be divided into two sets with no intersection, and the linkage between nodes divides the first and last nodes into different sets; in other words, no two nodes associated with any linkage belong to the same set.Many real-life scenarios can be mapped and transformed into bipartite graphs, among which associative relationships are a very important example because they have deeply penetrated all aspects of our daily life; for example, for students, the act of learning a course connects two very large collections of student groups and course lists, and the value of the data contained in this complex network that is perfectly abstracted into a bipartite graph is very large [12]. The value of the data contained in this complex network, which is perfectly abstracted into a bipartite graph, is very large. For example, movie viewing behaviour, personal travel choice behaviour, purchase behaviour on e-commerce websites, and song listening behaviour on music platforms are typical examples of association relationships [13]. The main process of the algorithm is to assign a certain energy value to each point in the X-set which needs to calculate the similarity, and then each point divides its energy value by its degree and then passes the average assigned energy to the points in the Y-set which are connected to it [14]. This process is synchronized and thus gives the algorithm the inherent property of being able to parallelize the process. After the first step, each point in the Y-set receives the energy from a different point in the X-set, and the next step is to reverse the energy flow and transfer it in the same way. Finally, the energy matrix is used to calculate the similarity between the nodes, which is used as the basis for the final recommendation policy [15].It has higher accuracy than the traditional recommendation algorithm. Of course, the data sparsity problem is particularly obvious when considering only the connection relationship in the recommendation system, so how to use more feature data in the process of algorithm implementation and optimization to improve the accuracy of the algorithm is very important to research direction. The core of this thesis is to explore the use of graph computation processing technology to solve the problem of personalized education in the field of education. The innovations in this research include a set of optimization solutions based on existing solutions for the cold start and data sparsity problems, as well as an improved and optimized two-part graph recommendation algorithm based on the plain two-part graph conceptual model, and the concept of energy flow, including the addition of the algorithm, is based on the concept of node degree, a set of recommendation value influence strategies for node degree and rating data, and the concept of user similarity in the flow of the algorithm.


## 1.1. Status of Research

Vehlken proposed the “STEM Education Program” in the United States as an educational program that encourages students to major in science, technology, engineering, and mathematics [6]. An et al. proposed a network combining the advantages of CNN and Factorisation-machine-supported Neural Networks (FNN) to solve the shortcomings of traditional recommendation algorithms by using auxiliary information such as text to provide additional information, which effectively solved the problem of data sparsity [7]. Yu et al. proposed a sparse edge noise reduction automatic coding model to extract product features from product review texts by combining user reviews with ratings, and, at the same time, the target user may be influenced by neighbouring users by adding this influence element into the model and proposed a recommended method of sparse edge noise reduction automatic coding by adding the influence factor of neighbouring users [8]. The experimental results show a significant improvement compared with the traditional model. Haubeck et al. proposed a recommendation hybrid model combining collaborative filtering and content based on the academic search and data mining platform Amine for a personalized recommendation to users [9]. They introduced a word vector model by incorporating deep learning methods, mapping users and papers to a word vector space, and using Word Mover Distance (WMD) to calculate similarity [10]. In recent years, more and more scholars have been devoted to the study and understanding of complex networks, and the most popular research is on bipartite graphs [11]. The core feature of bipartite graphs is that all nodes in a complex network can be divided into two sets with no intersection, and the linkage between nodes divides the first and last nodes into different sets; in other words, no two nodes associated with any linkage belong to the same set.Many real-life scenarios can be mapped and transformed into bipartite graphs, among which associative relationships are a very important example because they have deeply penetrated all aspects of our daily life; for example, for students, the act of learning a course connects two very large collections of student groups and course lists, and the value of the data contained in this complex network that is perfectly abstracted into a bipartite graph is very large [12]. The value of the data contained in this complex network, which is perfectly abstracted into a bipartite graph, is very large. For example, movie viewing behaviour, personal travel choice behaviour, purchase behaviour on e-commerce websites, and song listening behaviour on music platforms are typical examples of association relationships [13]. The main process of the algorithm is to assign a certain energy value to each point in the X-set which needs to calculate the similarity, and then each point divides its energy value by its degree and then passes the average assigned energy to the points in the Y-set which are connected to it [14]. This process is synchronized and thus gives the algorithm the inherent property of being able to parallelize the process. After the first step, each point in the Y-set receives the energy from a different point in the X-set, and the next step is to reverse the energy flow and transfer it in the same way. Finally, the energy matrix is used to calculate the similarity between the nodes, which is used as the basis for the final recommendation policy [15].It has higher accuracy than the traditional recommendation algorithm. Of course, the data sparsity problem is particularly obvious when considering only the connection relationship in the recommendation system, so how to use more feature data in the process of algorithm implementation and optimization to improve the accuracy of the algorithm is very important to research direction. The core of this thesis is to explore the use of graph computation processing technology to solve the problem of personalized education in the field of education. The innovations in this research include a set of optimization solutions based on existing solutions for the cold start and data sparsity problems, as well as an improved and optimized two-part graph recommendation algorithm based on the plain two-part graph conceptual model, and the concept of energy flow, including the addition of the algorithm, is based on the concept of node degree, a set of recommendation value influence strategies for node degree and rating data, and the concept of user similarity in the flow of the algorithm.


## 2. Stable Parallel Algorithm Design for Interdisciplinary Computer Online Education

Since deep neural networks are high-dimensional and nonconvex in the solution space, the current optimal model of deep neural networks cannot be solved directly by an analytic solution, but the approximate optimal solution is obtained by a heuristic search algorithm. The current solution of deep neural networks mainly uses the gradient descent method, and the process of solving a model can be divided into three major steps. In the forward propagation process, the model reads the input into the solution output and then calculates the distance between the output and the data labels as the loss, after which the gradient corresponding to the training parameters of the previous layers is derived by chaining, and finally the optimizer uses the gradient information to update the parameters of each layer according to a specific optimization algorithm [16]. One of the most widely used optimization algorithms in industrial production and scientific research is the small-batch gradient descent method, which is currently often referred to as stochastic gradient descent (SGD), and its standard formula is(1)wt+1=wt+λ∇w1B∑x∈BJx,wt.S is a randomly sampled subset from the training set, often called a mini-batch, w is the trainable parameter in the model, λ is the learning rate, and x is the input labelled sample; 1/B∑x∈BJx,wt denotes the loss average of a certain loss function over the data batch.(2)wt+1=wt+λ1k∇w1B1∑x∈B1Jx,wt−∇w1B2∑x∈B2Jx,wt.Equation (2) is also known as the simultaneous stochastic gradient descent method. The current deep neural network training task is usually the number of iterations required to traverse the data set offline, and an obvious strategy is to increase the batch size. However, in practice, the batch size on a single device cannot be scaled up indefinitely due to the limitation of the device’s memory size. However, the above equation demonstrates that when the batch size is too large, the data batch can be divided into multiple nonoverlapping subsets and given to multiple devices to perform forward propagation and gradient derivation alone, and finally the global gradient is obtained by summing the individual local gradients [17]. The effectiveness of the stochastic gradient descent method can still be guaranteed in this way. Therefore, the above after the derivation of the formula proves theoretically that deep learning is feasible using data parallelism for training, as shown in Figure 1.Figure 1
Data-parallel topology diagram.The core idea of the content-based recommendation algorithm is to mine the relevance or similarity of the metadata describing the content characteristics of the recommended course and then find the most similar course to recommend to the user by analysing the historical learning rating records of the student user who currently needs to make the recommendation. This algorithm is the most widely used core algorithm in the early days of recommendation systems [18]. The content-based recommendation algorithm does not focus on the specific rating data of the student use for the course; its core concern is to describe each course with the most appropriate feature attributes, so the selection of feature attributes is a core point of the algorithm, and then use big data analysis technology to find the courses with similar feature attributes to recommend to the user.Collaborative filtering recommendation (CFR) technology is the most widely used recommendation algorithm among many recommendation systems today. It is divided into user-based collaborative filtering and item-based collaborative filtering. An important difference between deep learning and machine learning is the size of the data. Many experiments and works have shown that the size of data directly affects the performance of deep learning. We all hope to achieve good results with small datasets and simple algorithms, but the current fact is that deep learning on small datasets is often prone to overfitting. The essence of collaborative filtering is to generate recommendation lists by calculating similarity. The nearest neighbour technique is the core concept of this algorithm, which mainly uses student users’ ratings and learning records of course items to calculate the similarity between different student users. By analysing and integrating the rating data of several student users who are closest to the recommended target users for other courses, the final list of alternative recommended courses is given after weighted average and filtering.The User-Based collaborative filtering algorithm first must find other users who are like the new user based on the user’s historical behaviour information; meanwhile, it predicts the items that the current new user may like based on the rating information of these similar users on other items. Given the user rating data matrixR, the user-based collaborative filtering algorithm needs to define the similarity function s: U × U ⟶ R to calculate the similarity between users and then calculate the recommendation results based on the rating data and the similarity matrix. Item-Based collaborative filtering algorithm is another common algorithm. Unlike the User-Based collaborative filtering algorithm, the Item-Based collaborative filtering algorithm calculates the similarity between Items to predict user ratings. This means that the algorithm can calculate the similarity between Items in advance so that the performance can be improved. The Item-Based collaborative filtering algorithm uses the user rating data and the calculated Item similarity matrix so that the target Item can be predicted.Based on the above findings, if a larger learning rate is used, then this may lead to instability problems during the training of the deep neural network model. Although the above learning rate warm-up strategy tries to avoid this problem by using a smaller learning rate in the initial phase of deep neural network training, it still cannot solve the problem of large differences in L2 parametric ratios between individual network layers, so the layer-wise learning adaptive rate scaling (LARS) strategy is born. The purpose of layer-wise learning adaptive rate scaling is to introduce a local learning rate for each layer of a deep neural network.(3)Δwti=λ∗γi∗∇wiJwt,where λ denotes the global learning rate and γi denotes the local learning rate (or learning rate deflator) at layer i. The local learning rate at each layer is defined by a confidence factorη that represents the extent to which the global learning rate is expected to change; γi is defined in the following way:(4)γi=η∗wti∇wiJwt,η<1.Also, for compatibility with gradient recession, we have the following equation:(5)γi=η∗wtj∇wiJwt+β∇wiJwt,η<1.Data parallelism, model parallelism, and hybrid parallelism, as well as a detailed analysis comparing the differences and application scenarios among various distributed training approaches, are presented. The full normalized communication strategies commonly used in distributed training are also introduced in detail. Also, many training strategies for low precision scenarios are proposed to accelerate the training process of deep neural networks. To avoid the problem of poor convergence of the model training due to the use of large batch data, this chapter also introduces the current mainstream strategies for training in large-scale distributed scenarios.Compared with parametric server architecture, it has better horizontal scalability because it removes the dependence on the central node and does not require additional resources to be deployed as parametric servers. However, the full normalization approach is limited by the slowest node (network or compute) in the whole computing system, and there is a shortboard effect, which requires all nodes in the computing system to maintain the same configuration; otherwise, the nodes with high arithmetic power will be limited by the nodes with low arithmetic power. Also, the full normalization method is not as flexible as the parametric server architecture for handling node failures in the computing system. The system is calculated by a certain algorithm to find the result of the function; that is, the set of system items with the largest predicted value of user interest is finally recommended to the user.(6)∀u∈U,Ou=argminpu,o.Combined with the research content and direction of this topic, formula (6) is defined and reexpressed, and o of formula (6) corresponds to the representation of the main carrier video resources in our research topic, so the above formula is applied to this paper to form the definition of the video recommendation system which can be derived from formula (6) and expressed in the following formula:(7)∀u∈U,vu=argminpu,v.The platform analyses the basic demographic information attributes of student users and the behaviours logs of students in the process of using the platform and refines the user behaviours to obtain a relatively accurate user interest model, while considering the user Ebbinghaus memory curve to increase the factor change of user interest decay, and finally form a system that meets the requirements of the system model and can formally express the user demand information. At the same time, the system predicts the interest level of video resources according to similar users and combines the knowledge model in the learning field, using the recommendation algorithm as a bridge between users and video resources, so that high-quality video resources can be effectively exposed, and, finally, it can realize the analysis of user behaviours, accurately grasp user needs, and make the recommendation results in more effective and personalized output.The content of learning or learners are described in a systematic structured way, and, through this net-like way, students’ learning goals and individual students are combined, and the entire set of learning contents is linked to each knowledge point, and the learning resources are accurately described to make the whole knowledge model more perfect, so that students can get more diversified learning content according to their individual needs, which also enables the effective integration and reuse of educational resources, breaking the original physical barrier, which is of great significance to the development of online education, and then serves as an important influence factor in the recommendation system of the smart education platform to recommend more capable goal-oriented video results to users, as shown in Figure2.Figure 2
Distribution of weight parameters/gradients.In Figure2, we can see that the corresponding values under different eigenvalues will also have different values and there is no corresponding pattern. In the whole process of intelligent personalized recommendation, the user’s behaviours information is a crucial part and the basis of the whole process. The user’s behavioural information will eventually be expressed mathematically to form a model for the individual user, and this model will be updated iteratively with the user’s behavioural information and time to ensure that the user’s interest is real and in real time. Then the system wants to form a real model of the user, that is, the user’s real needs and preferences. There are two ways to get it: one is the system through selective or according to the public user characteristics and product characteristics information presented by the user to take the initiative to choose, to inform the system [19]. The other way is that the system collects the user’s behavioural information through buried points for different functions to dig.User base information and user behaviours information are generated in the system. The basic information of users is directly imported from the student information stored in the education system without passive input from users, and the information of users’ operation behaviours is mainly reflected by the data buried in the logs of the system at the development of the system, and the content of the logs generated by users’ behaviours is reported. The feedback behaviours of users are mainly required to actively provide feedback to the system, such as the basic information of user registration or user rating for a certain function, user comments for a certain item, and so forth. This approach is convenient and easy to operate and is often a natural attribute of the user. This approach itself has an antihuman factor because it needs to lead the user to participate, so the final result is often not as satisfactory and for the user may involve privacy users are afraid of being leaked or used for other purposes, resulting in users being reluctant to actively express their personal preferences to the system. Therefore, the access to user information in this way may be minimal. Moreover, if the user cannot give feedback quickly after the change of such information, it may also produce bias and thus the effectiveness cannot be guaranteed. The most important thing is that the process of collecting information can lead to user sensitivity and make users resentful. This makes the user experience worse and eventually leads to user departure.


## 3. Real-World Problem Scenario Design for STEM Education

STEM literacy includes science literacy, technology literacy, engineering literacy, and math literacy. Among them, scientific literacy is the ability to understand the laws and theorems of science, to use scientific understanding and thinking and technology to explain observed phenomena, to improve understanding of the natural world, and to solve real-world problems. Technological literacy is the ability to use, manage, understand, and evaluate technology, and it includes three dimensions: technical knowledge, technical ability, and the way to think and act technologically. Engineering literacy refers to the ability to master certain engineering knowledge as well as the ability to solve problems involving engineering knowledge in daily life and to have scientific, rational, and independent judgment and choice. It includes four dimensions: engineering knowledge, engineering ability, engineering consciousness, and engineering ethics. Mathematical literacy refers to the mathematical content knowledge possessed as well as the understanding of mathematical knowledge and the mathematical thinking methods and abilities demonstrated in solving problems.STEM education is comprehensive in that it links the four disciplines of mathematics, engineering, technology, and science education. STEM education believes that knowledge is dynamic and that changes in the way the curriculum is implemented and the dynamic nature of the content are what help students expand and refine their knowledge systems [20]. The practicality is reflected in the very formulation of STEM education and the specific implementation of the curriculum. On the one hand, STEM education emphasizes the importance of technology, engineering, mathematics, and science; on the other hand, STEM education uses learning activities as the basis for problem-solving-based learning, thus giving learners a hands-on classroom experience. The richness of STEM education is mainly reflected in the requirement for multidimensional goals, setting up an integrated curriculum and interpreting its richness from multiple perspectives based on the characteristics of the original disciplines. The richness of STEM education is mainly reflected in the requirement for multidimensional goals, the setting of an integrated curriculum, and the interpretation of its richness from multiple perspectives based on the characteristics of the original subjects, as well as the promotion of multiple possibilities for learner’s self-development through a diverse curriculum.Project-based teaching focuses on student autonomy and provides students with planned guidance for action and problem-solving based on the construction of authentic situations for them. The content of project teaching should be complex, multidisciplinary problems expressed in real life and situations based on the current disciplinary knowledge background. As can be seen, the STEM education philosophy and project-based teaching share many common features in that they both point to real-world problem-solving. There is often no single problem that exists in real-world situations; problems are complex and diverse, and learners are usually confronted with a range of problems. The same is true for problems in project-based activities. Project-based instruction places greater emphasis on providing learners with real-world experiences and relies on project practice to help students gain a deeper understanding of concepts related to various fields of science, technology, engineering, and mathematics.Curriculum standards define the tasks and purposes of teaching the subject and are the programmatic documents of education. Teachers should take the requirements of the curriculum standards into full consideration when choosing the topics for their projects, and they should choose the project topics under the outline of the curriculum standards. To ensure that the whole project is carried out smoothly, the knowledge points contained in the project theme should not exceed the knowledge range required by the curriculum standards too much. It should be based on the students’ original foundation, which they can master and acquire through their efforts while paying attention to the completion within the specified class time. Therefore, the teaching content is selected with attention to the scope of the teaching content, as shown in Figure3.Figure 3
Overall system structure design diagram.Resources and tools are needed to support the project process. Tools and resources are essential to the implementation of the project. Teachers should take into account the school’s existing teaching environment and the resources and tools available to them when designing the content of the project theme.Based on the STEM concept, this study identifies the project themes through preliminary analysis and selects knowledge related to the project themes from math, science, and technology disciplines to summarize and organize them. The project content was constructed from the relevant knowledge, and the content was integrated into the implementation of the robotics project so that different disciplines could work together to serve the project [2, 21–27]. Based on the analysis of the previous teaching, the authors designed 11 project themes for some of the functions and modules of the robotics kit, combining the principles of project theme design. The projects involved knowledge of the robotics disciplines, including understanding the components and functions of the ubiquitous robot, mastering the connection between the servo and the master controller, understanding the principal knowledge and functions of the various sensors in the robot, and learning to write programs to implement the functions of the robot sensors. The projects are divided into basic and comprehensive projects according to the number and difficulty of the knowledge points they contain, as shown in Table 1.Table 1
Project theme design.
Type numberFeaturesValues15511225223683341444598556876676677The basic project focuses on the “structure and function” part of the middle-school robotics module in the curriculum standards. The project knowledge includes basic hardware knowledge such as robot components, sensors, and servos, as well as simple programming knowledge such as flowcharts, program structure, and graphical programming. The interdisciplinary knowledge points refer to the knowledge points of various disciplines that may be included in the project. The determination of the interdisciplinary knowledge points requires the combination of relevant knowledge points from multiple disciplines involved in the current topic. From this idea, the authors compose the interdisciplinary knowledge points of the project from science, technology, engineering, and mathematics, respectively. Each item in the basic project involves relatively simple knowledge points, and students can use the learning resources to explore and learn under the guidance of the teacher, as shown in Figure4.Figure 4
Flowchart of project teaching activities.The teaching environment in this study consisted of both physical and digital environments. In the physical environment, the robotics course was conducted in a dedicated robotics lab at the school with nine hexagonal tables suitable for group activities, which served as a robot building and debugging area. The school has 18 entry-level kits and 20 advanced kits for students to build. For the digital environment, the robotics classroom has a tablet for each robotics kit with the EDU Ubiquitous App, a whiteboard, and six computers for information retrieval. To successfully carry out the project, teachers need to provide students with learning resources at appropriate times to meet their inquiry needs. In this study, the design of teaching resources for the identified project topics was combined with the connotations and characteristics of STEM education. First, the resources should be interdisciplinary, integrating STEM-related knowledge and skills from the robotics discipline. Second, the resources should be interesting, so that real-life situations can be designed as learning activities to stimulate students’ interest in the inquiry.


## 4. Results and Discussion

This experiment uses the classical large-scale image classification task dataset. The dataset is divided into 1.28 million training samples as the training set and 50,000 validation/test samples as the validation set, and the whole dataset contains a total of 1000 category labels with a balanced distribution of categories in the training and test sets. All experimental models are randomly initialized with trainable parameters, and then the training operation is performed on the training set, while the prediction is performed on the validation set, and the Top-1 accuracy is calculated from the prediction results as an objective evaluation index of the statistical performance of the model. In the experiments of this paper, all image data are stored in the original image format in all computing nodes, while in the actual training different nodes will take out the training samples from the same training set without putting them back according to specific sampling rules, thus ensuring that the same data samples will not be taken between different nodes. The preprocessing part of the data uses the standard preprocessing operations provided by PyTorch, including random rotation, cropping, and normalization, as shown in Figure5.Figure 5
Basic information of the model.Although mixed precision is of limited help to improve the scaling efficiency of distributed training in the current system, it can still effectively improve the throughput of the model training process. ResNet-50 trained with mixed precision can process up to 12,700 training samples per second in a 32 GPU configuration, while MobileNet-v1 trained with mixed precision can process up to 37,000 training samples per second in a 32 GPU configuration. The biggest benefit of the throughput improvement with mixed-precision training is that the training time of the model can be greatly reduced. Figure6 shows the training time of ResNet-50 and MobileNet-vl on a dataset trained in data parallel on 32 RTX 2080 Ti. It can be seen that ResNet-50 is faster in mixed-precision mode than in single-precision mode by more than an hour when the training data batch size is 2048 and the number of training iterations is 95. When the batch size of ResNet-50 is further expanded to 4096, the training time can be further reduced to 3 hours and 26 minutes.Figure 6
Training acceleration ratio.The “single-precision” and “mixed-precision” groups of MobileNet-v1 show the same results as ResNet-50, as shown in Figure7, although the Top-1 accuracy curves of the two groups show completely different oscillations at the beginning. Although the Top-1 accuracy curves of the two groups show completely different oscillations at the beginning, they keep the same trend and gradually converge together after 125 iterations of random oscillations, finally reaching an accuracy of 71.5%, which is slightly higher than the official 70.9%. It is worth noting that the “semiaccurate” experimental group of MobileNet-v1 started to diverge after 20 iterations and completely diverged and failed to converge after about the 30th iteration. This is another strong demonstration of the effectiveness and stability of mixed-precision training.Figure 7
Accuracy graph.Firstly, we introduce the hardware and software environment for the experiments, and then we show and analyse the training acceleration effect and the scaling performance under the distributed training based on the proposed distributed training strategy on ResNet-50 and MobileNet-vl, and the experimental results of the side-by-side comparison of the scaling performance with the benchmark system show that the proposed distributed training strategy can effectively improve the training efficiency of ResNet-50 and MobileNet-v1 network models. The experimental results show that the proposed distributed training strategy can effectively improve the training efficiency of ResNet-50 and MobileNet-v1 network models with multiple machines and multiple cards. Besides, this chapter also analyses that the distributed training extension does not significantly impair the convergence of the model training by the training accuracy curve of the model. It is also worth mentioning that this work finally achieves a Top-1 accuracy rate higher than the official baseline without changing the structure of the ResNet-50 and MobileNet-vl models with this system.At the end of the robotics practice, written interviews were conducted with students in the form of a questionnaire to better understand whether the STEM-based robotics program was beneficial to the learning of other subjects. Many students indicated that the robotics project had a significant impact on the learning of other subjects, as shown in Figure8.Figure 8
Analysis of learning other subjects.As can be seen in Figure8, 66.7% of the learners agreed that the robotics project implementation utilized knowledge from other disciplines, 87.5% of the learners believed that the robotics project learning helped to learn about other disciplines, 83.3% of the learners believed that the robotics project learning helped to understand other disciplines, and 70.8% of the learners believed that the knowledge they learned in the robotics classroom could be applied in other areas. In summary, the STEM-based robotics program had a significant impact on the learning of other disciplines. Based on the project theme, the instructional design model of the project was constructed, and the project theme was designed based on the instructional design model. In the teaching design of the project, attention should be paid to the creation of the context and the design of relevant inquiry activities according to the requirements of teaching objectives, the characteristics of students’ physical and mental development, and the existing teaching conditions. In the design of teaching resources, they need to be determined and designed through each stage and students’ needs. Too many learning resources cannot stimulate students’ inquiry ability, and too few learning resources cannot meet students’ inquiry needs. In terms of learning assessment design, the design of teaching assessment under the STEM concept should be based on four aspects: science objectives, technology objectives, engineering objectives, and mathematics objectives. When designing the assessment, the role of students in the assessment should be emphasized, and multiple forms of formative and summative assessment should be conducted.


## 5. Conclusions

The action research method was used to analyse the teaching objectives, learner characteristics, learning contents, and characteristics of robotics subjects in the robotics curriculum. The traditional collaborative filtering recommendation algorithm is widely used and mature, but it is not applicable to graph computing technology, and the time complexity of the traditional recommendation algorithm increases dramatically with the increase of data level in the face of a large amount of data. Finally, a recommendation algorithm based on the bipartite graph is introduced, which is the core algorithm used in this thesis to build the recommendation system because of its inherent applicability to graph computing technology. The core data structure and basic algorithm concepts of the two-part graph algorithm are then described in detail, and several optimization points are proposed on top of this, including the use of a new random walk strategy and the addition of the concept of node degree to the influence factor of the algorithm execution process, and a new set of scoring data and the influence of node degree on the final recommendation value strategy are applied. The final experimental results show that the improved bipartite graph-based recommendation algorithm has a different degree of advantage over the traditional recommendation algorithm in terms of accuracy and recall by more than 20%.



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