Network Course Research Articles

Initial validation and feasibility of a Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course for wheelchairs

Many Canadian manual wheelchair users face many challenges in winter months such as slippage on ice- or snow-covered ramps, snow windrows, and casters becoming stuck in deep or hard packed snow. These barriers impact wheelchair users’ ability to participate in the community. This study aimed to evaluate the validity and feasibility of a winter wheelchair obstacle course known as the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN). The results demonstrated that the SNOWMAN course authentically represented real-world winter conditions, as confirmed by participant responses and qualitative feedback from four manual wheelchair users. The course was comprehensive, covering a range of winter obstacles typically encountered by wheelchair users. Construct validity was established by differentiating performance between manual wheelchairs and a motorized platform with snow tracks, showcasing varying completion times and device satisfaction levels. Feasibility was also assessed, with the administration protocol being mostly adhered to, safety measures implemented, and usability scores meeting acceptable thresholds. The SNOWMAN course showed promise for evaluating wheelchair adaptations and devices for winter conditions, as well as training users in winter mobility skills. Future research directions include comparing different wheeled mobility devices, exploring adaptations for usability in winter, and developing new technology tailored for challenging terrains and winter conditions. The SNOWMAN course could serve as a valuable tool for both research and clinical applications in enhancing winter mobility for wheelchair users.

Vilanets: An advanced virtual learning environments to improve higher education students’ learning achievement in computer network course

Vilanets: An advanced virtual learning environments to improve higher education students’ learning achievement in computer network course

The Construction of an English Network Course Based on the Theory of Hybrid Real-Time Synchronization Algorithm

At present, the construction of online courses is an important content in the process of educational informatization. At present, there are two problems in the construction of online courses: from the teaching point of view, the design of teaching activities and teaching processes in online courses is not enough, and the teaching effect is not good. Technically, the development of most courses is basically one-off and cannot be updated quickly. Due to the small scale, limited access and strong pertinence of SPOC, it has obvious advantages to implement the online and offline mixed teaching mode of college English based on SPOC in college English teaching. This paper mainly studies the research and analysis of English online courses based on the hybrid real-time synchronization algorithm. This paper combines OFDM synchronization algorithm with SPOC hybrid algorithm teaching to analyze and study it. According to the research, the influence of English online teaching course is very great, and the effect is more obvious than that of traditional teaching.

Symposium 2: Networked Learning for Life-Long Learning in Swedish Health Care

A healthy work environment in health care requires that staff feel in control and are provided with self-development opportunities, information and adequate resources, all of which lead to job satisfaction. Lifelong learning for health care staff has received increasing attention as a step towards an attractive workplace and safer care. However, people in working life demand conditions that enable them to participate in educational activities. The aim of this paper is to describe experiences of creating and distributing short, flexible and networked courses in higher education, for health care personnel’s lifelong learning. Following research questions are included: what challenges have been identified by higher education staff when creating flexible courses for lifelong learning based on networked learning? and what, in the course design, was essential for the students networked learning? The project was built on the assumption that knowledge is empowering for staff and patients, and that organizational learning and behaviour is central for quality. The sociocultural theory of learning, which claims that all humans are willing and able to learn, and that people learn when they realize that the knowledge is relevant and important to them, has inspired. One pilot course was developed, and this paper focuses on the preliminary findings for the course “Peer learning”. E-mail, memos and notes were used as data and examined with thematic analysis. The findings showed four themes that answered the aim: identifying courses in collaboration; recruiting and maintaining the participants; learning activities in the course design and improving the course. It was important to have flexibility along with some structure in the course. The three components in networked learning contributed to learning and motivation during the course: relationship between educators, gatekeepers and students, the technology in the digital classroom, and a collaborative engagement. It seems successful working together and networking between the two organizations (university and health care) for a common purpose. The conclusion focuses further on the importance of building a robust network as well as bringing new ideas and needs for lifelong learning from the health care to the university. Further data collection is needed to get additional and deeper insights of health care managers along with workforce experiences and wishes for lifelong learning.

Augmented Reality Learning Media Application In Computer Networking Courses

In computer network learning, there is still little use of media which has an impact on students' understanding of device material and computer network topology. Augmented Reality (AR) based learning media can answer these problems by providing dynamic visualization and interactive simulations. The research objective is that AR applications can be used to help visualize abstract concepts for understanding and structure of an object model. The development method used is MDLC (Multimedia Development Life Cycle) which consists of six stages, namely concept, design, material collecting, assembly, testing, and distribution. The results of the AR application research show that the value of the learning media application in terms of material is declared valid at 0.85 and in terms of design it is declared valid at 0.86. The AR application was also stated to be very practical, this can be seen from the responses of lecturers and students with the practicality of the learning media application being 87% as seen from ease, motivation, attractiveness, and usefulness. From the results of this research, the AR learning media application is very practical to apply to students, especially in computer networking courses.

Exploration of Deep Learning and Neural Network Course Teaching Reform in Finance and Economics Colleges under the Background of New Engineering

Exploration of Deep Learning and Neural Network Course Teaching Reform in Finance and Economics Colleges under the Background of New Engineering

Analysis of Computer Network Courses Security Precautions Based on Big Data

Analysis of Computer Network Courses Security Precautions Based on Big Data

THỰC TRẠNG LỰA CHỌN PHƯƠNG PHÁP KIỂM TRA, ĐÁNH GIÁ NHẰM NÂNG CAO CHẤT LƯỢNG TỰ HỌC CHO HỌC PHẦN MẠNG MÁY TÍNH

Teaching and learning under the credit-based system has become widespread in the higher education system of Vietnam. The assessment of learning outcomes through credit-based education not only relies on the foundational knowledge accumulated in the classroom but also evaluates the process of self-study and research of learners. Besides spending time studying and researching at the university, students are required to allocate a significant amount of time to conduct self-research and participate in group discussions to address issues related to the subject matter. Although most students demonstrate a high level of self-awareness during the learning process, there are still instances of a lack of self-awareness. Recognizing this issue, the question arises: how to guide students to apply self-research methods effectively and how to objectively evaluate the results of the self-research process? Drawing on practical experience in teaching the Computer Networking course under the credit-based system for students of the K22 cohort, belonging to the Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology, the authors have conducted exchanges and proposed methods, organizing techniques, and assessment tools for evaluating the results of the self-research process of the course content, aiming to enhance the quality of education and encourage students to actively engage in the learning and in-depth research process.

Hybrid data clustering algorithm and interactive experience in E-learning electronic course simulation of legal education

With the development of multimedia technology, interactive entertainment experience is an important feature of online teaching mode. Students can engage in interactive learning through various mobile apps and online tools. E-learning provides an innovative way of learning that enables distance learning through electronic course simulation. The objective of this research is to design and develop an improved E-learning course simulation for law teaching by applying hybrid data clustering algorithms and enhancing interactive experiences to improve student engagement and learning outcomes. This article analyzes the simulation of legal education E-learning electronic courses based on mixed data clustering algorithms and interactive experiences. On the basis of the mixed data clustering model of the k-means algorithm, we propose a probability based mixed clustering model that can effectively handle the clustering problem of mixed data. Based on this algorithm, we have established a legal teaching network system platform based on artificial intelligence. The hybrid data clustering algorithm solves the instability problem of the k-means algorithm in the construction of the legal teaching network course platform, providing a reliable solution for the development of artificial intelligence network courses. Based on teaching concepts and combined with various technological characteristics such as network technology and multimedia technology in the information age, users who pass through this teaching system can fully utilize the teaching resources and various functions in the system. The hybrid data clustering algorithm proposed in this article has good clustering performance.

Development of the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course.

Manual wheelchairs (MWC) users have limited mobility during winter months as they encounter many environmental barriers that restrict their community participation. This paper outlines the creation and standardization of an outdoor environment designed to simulate the real-life conditions and obstacles experienced by MWC users in winter. This study consisted of four phases. In Phase 1, researchers used a qualitative ethnographic approach to document the specific challenges and adaptive strategies used by MWC users in winter conditions. In Phase 2, key informants with expertise in MWC winter mobility were invited to co-design the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course. Participants reviewed draft design solutions and offered their input and suggestions to expand upon the initial design. A second co-design workshop included additional key informants, including service providers, policymakers, and professionals with expertise in landscape architecture and engineering, to validate the design solution. The workshops resulted in a detailed illustration of the SNOWMAN course, including five sections: platforms with side slopes, a miniature ice rink, curbs and curb cuts, a path with uneven winter surfaces, and modular ramps at various slopes. Phases 3 and 4 marked the conclusion of the study and involved fabrication of the SNOWMAN course and establishment of a standardized protocol for course setup and maintenance. The project aimed to offer several additional potential benefits, supported by the various stakeholders across the study phases, that extend beyond creation of a controlled and safe environment for wheelchair users to develop their winter mobility skills. Practicing wheelchair skills in this area may assist wheelchair users in gaining confidence which may ultimately translate to increased participation in the community.

Professionally oriented network course as the basis for developing the computational thinking of future engineers

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Preparation of final qualifying work at a university: the view of modern students

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Computer Education Curriculum Innovation Based on Flipped Classroom and Network Education Model

This article conducts a series of research on computer network courses based on “flipped classroom.” Teaching reform will be carried out for computer networks, but the results are mediocre. The author of this article has carefully considered and summarized computer network teaching based on his years of teaching experience and has put forward suggestions for widespread application. This article discusses the application of translation. The necessity and connotation of transitioning to classroom mode were emphasized, and the design of this teaching method was emphasized, adopting a three-step approach of “consolidating and improving knowledge after class.” Therefore, teachers should start from their own perspective, based on process preview, in class learning, and after class review, find the connection point between flipped classroom and computer network courses, reflect on the problems in teaching, and adopt targeted teaching methods to solve problems. This article provides guidance for improving teachers' teaching level.

Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network optimized with Artificial Hummingbird Optimization Algorithm

Music Tone Synthesis is an applied science or method that implies to identify and study of specific orchestral tone currently part of a music. It is especially effective in the area of oral music training system classes, somewhere be able to support in the practice and growth of musicians. Music Tone Synthesis provides singer and writer to produce large number of noises as well as imitate several tools and impact it cannot be flexible otherwise realistic for create over standard classical instrument. In this manuscript, Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network enhanced with artificial hummingbird Optimization algorithm (MTS-AIDINN-AHOA) is proposed. The input data are obtained from the audio signal. Then the data are pre-processing using Stein Particle Filtering (SPF) to remove the noise. The pre-processed data is given into the Two-sided Offset Quaternion Linear Canonical transform (TSOQLCT) for extracting the musical features such as melody, harmony, tempo, and dynamics. After this the extracted feature is provided to the Anti-Interference Dynamic Integral Neural Network (AIDINN) is used for the music tone synthesis and it is classified as pitch, chronaxie, volume, tone color. In general, the Anti-Interference Dynamic Integral Neural Network (AIDINN) does no express adapting optimization strategies to determine ideal parameters to assure precise prediction. Thus, it is proposed to utilize the Artificial Hummingbird Optimization Algorithm enhancement AIDINN for Music Tone Synthesis. The proposed MTS-AIDINN-AHOA method is implemented on MATLAB. Then performance of proposed technique is evaluated to other existing techniques. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy, 19.23%, 23.56%, and 33.96% higher precision, 26.28%, 31.26%, and 19.66%higher recall, 28.96%, 33.21% and 23.89%higher specificity comparing with the existing methods such as a research on Musical Tone Recognition Method Based on Improved RNN for Vocal Music Teaching Network Courses (MTS-RNN), Music Timbre Extracted from Audio Signal Features (MTS-BPNN)and Feature Extraction and Categorization of Music Content Based on Deep Learning(MTS-SMNN) respectively.

NETWORKING AS ONE OF THE WAYS TO DEVELOP SOFT SKILLS OF STUDENTS

This article examines the problem of developing soft skills of students, which are important competencies that help a person to communicate better and more effectively and, in the future, to work successfully. It has been established that soft skills, for example in the field of communication, can be learned at university, but this requires modern technologies such as networking and digital networking, the effectiveness of which has been proven in developed countries of the world. The problems and basic networking skills needed by students to carry out soft communication activities are identified. The effectiveness of networking as a way to develop soft skills of students is implemented within the framework of the “Networking” development program to develop the art of communication and soft communication skills in three modules: “Forum Theater”, “Art of Self-Presentation” and “Business Games”. The carried out activities are aimed at increasing students’ levels of communication skills, communicative competence, and sociability. Its effectiveness is confirmed by the results of experimental work using three methods, as the soft communication skills of many students in the experimental group who attended a special networking course received significant development, both in relation to communication skills, as well as communicative competencies and sociability.

Integration of craftsman spirit of great powers into the practical classroom teaching of computer network foundation

It is a trend in curriculum construction to extend ideological and political education from specialized ideological and political courses to curriculum ideological and political courses. Based on the characteristics of private applied undergraduate courses, this article takes the practice of "RJ-45 interface twisted pair" in computer network courses as an example to explore the spiritual power of ideological elements, and integrates curriculum education into teaching case design and teaching process to cultivate students' spiritual power and achieve the goal of comprehensive education.

Hardware-Free Network Internals Exploration

In undergraduate computer networking courses, the ideal scenario involves demonstrating network communications with multiple interconnected computers and a packet sniffer tool. However, practical challenges arise when attempting hands-on exercises, such as accessing or reconfiguring physical computers for online networking practice. Additionally, certain network concepts, like routing and switching, are typically discussed theoretically due to the limitations of observing external network packet transfers and the constraints faced by institutions in maintaining the necessary hardware for hands-on practice. This paper introduces a simulation-based approach to facilitate the teaching and learning of computer networking internals in an online environment, eliminating the need for dedicated hardware devices. The paper outlines various simulation activities and experiments designed to assist instructors in teaching and enable students to explore these internal networking concepts effectively.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

In recent years, with the rapid development of big data technology, more and more data are continuously generated with the summary of university systems. Therefore, how to use these educational data to provide more scientific decision-making information for university information builders is very important. This research collected various educational data through the network teaching system and campus information system. After that, the collected data was used to analyze the students' online behavior and to excavate valuable behavioral characteristics. In addition, the experiment also proposed indicators to describe students' behavior, such as network course behavior, network viscosity and life regularity, to provide the basis for the subsequent abnormal performance prediction model. Finally, the experiment used DBSCAN algorithm based on distance optimization for clustering analysis, and constructed a NA model based on multiple classifiers. The research results showed that when, the SC value was 0.711, which was the optimal solution of D-DBSCAN algorithm. At this time, the corresponding number of clusters was 4. When N=2, that was, the base classifier of NA model is composed of C4.5 model and SVM model, the prediction accuracy and time consumption are the most appropriate. The accuracy, recall and F1 values of NA model were 98.16\%, 97.26\% and 0.958 respectively, which was better than that of single model. To sum up, the NA model based on classifiers proposed in the study had higher accuracy and better model performance, can effectively reflect students' academic level, and could provide accurate abnormal performance data for college sports online teaching tests.

An Empirical Study of the BOPPPS Pedagogy in a Computer Networks Course and Strategies for Curriculum Reform

Abstract In the context of modern education, it is essential for the sustainable development of the computer discipline to continuously explore the general process of course teaching and the strategy of class reform through the practice of new teaching methods. This paper discusses the practical application of the BOPPPS model teaching method in computer network courses and the strategy for class reform. The BOPPPS teaching model creates six teaching sessions in the Computer Networks course, which include an introduction, learning objectives, pre-test, participatory learning, post-test, and summary. Finally, we used student performance analysis and student questionnaires to understand the teaching effect of this teaching model. The study’s findings revealed that the average score of the case study questions in the experimental class was 3.32 points higher than that of the control class after adopting the BOPPPS teaching mode. During the learning process, both 33.5% and 39.5% of students expressed their ability to clarify and recognize learning objectives and think independently.