Online-Journals.org (International Association of Online Engineering)
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Study the Effect of Using Google Classroom on the Academic Performance Under the Covid19 Pandemic Using Data Mining Technique
Get PDF— In light of the pandemic that has swept the world, the use of e-learning in educational institutions has become an urgent necessity for continued knowledge communication with students. Educational institutions can benefit from the free tools that Google provide and from these applications, Google classroom which is characterized by ease of use, but the efficiency of using Google classroom is affected by several variables not studied in previous studies Clearly, this study aimed to identify the use of Google classroom as a system for managing e-learning and the factors affecting the performance of students and lecturer. The data of this study were collected from 219 members of the faculty and students at the College of Administration and Economics at the University of Baghdad in Iraq and have reached Google classroom offers all the technologies wanted by students and lecturer but there is a positive correlation between lack of knowledge of technical matters and decrease in the use of Google classroom and online education and vice versa
Analysis of Device Mismatches Effect on the Performance of UWB Receiver Front-End in Wireless Body Area Network Sensor Nodes
Get PDFToday it is important to manufacture high quality integrated circuits which are insensitive to device mismatches. This paper presents an analysis of MOSFET transistors mismatches effect on the performance of UWB receiver front-end which constitute the most important part of Wireless Body Area Network sensor node. The receiver is based on Balun LNA with 25% fully differential double-balanced passive mixer. A PMOS and NMOS transistors mismatch models were proposed to determine LNA output offset voltage and mixer offset current respectively. The analysis result suggests that, to minimize NMOS current mismatch, and thus reducing second-order inter modulation distortion, the overdrive voltage must be maximized. A Monte Carlo and harmonic balance simulations were performed using 0.18µm CMOS process to evaluate the impact of mismatch as well as Vth mismatch on the receiver gain and IIP2. Simulation results show that IIP2 of the receiver is less sensitive to mixer NMOS mismatch but receiver gain is more sensitive. The receiver IIP2 confidence interval in case of NMOS mismatch is [24.674, 24.77]dBm and in case of NMOS Vth mismatch is [24.659, 24.857]dBm. This show the robustness of the proposed UWB receiver front end. Therefore the proposed circuit meets the requirement of UWB system perfectly which make it suitable for WBAN applications
Online Teaching in a Time of Crisis: Social Capital and Community Building Tools
Get PDFWe draw on Bourdieu's work on the sociology of education and introduce the idea of building social capital and community in the often misunderstood, one-sided narrative of online learning, which is seen as both an isolated and isolating experience. We also look at educational praxis, which is "informed, committed action," to address socially differentiated educational attainment, which is perceived to be more pronounced in online learning. To investigate this, we perform a narrative analysis of qualitative data from student evaluations of three online asynchronous courses taught within the past two years, where we introduced regular synchronous sessions. Our findings indicate that synchronous sessions, especially during the pandemic, were perceived by students as a cornerstone of a pedagogy of care. Further, the data suggest that these sessions work better than fully asynchronous courses for students prone to lower educational attainment due to uncontrollable conditions (e.g., socio-economic status, race) because of the additional layer of support. Finally, our findings indicate that these sessions represent one effective way for students to build social capital and community in courses that are otherwise fully asynchronous
Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System
Get PDFInternet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion
Investigation of Early-Stage Breast Cancer Detection using Quantum Neural Network
Get PDFaided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN
Industrial Engineering Student Competency Mediation Model: Analysis from the Perspective of Educational Psychology
Get PDFThe purpose of this study was to analyze the influence between the research competences of the context, decision-making and planning in a sample of students of the Industrial Engineering career of a private university in Lima - Peru. In the sample there took part 150 students of the career of Industrial Engineering, 84 men (53.9%) and 66 women (42.8%). The sample were between the 16 to 31 years old (MeanAge = 19.77, SD = 4.50). The results showed, the three competencies are significantly related and at the same time, they are predicted. Nevertheless, it is identified that the decision-making competence mediates the predictive relationship between the investigations of the context with the planning; in this way, it is understood that future industrial engineers can plan well if they have previously developed the competence of contextual research and decision-making
The Effect of Virtual Reality Game Teaching Technology on Students’ Immersion
No full textVR (Virtual Reality) technology as the current cutting-edge technology, is widely used in the field of education, and plays a huge advantage. VR game teaching creates an immersive learning environment so that learners can have a clearer and more intuitive understanding of professional knowledge of various disciplines, enhance their learning enthusiasm and interest in learning, and give full play to subjective initiative of learners. VR technology gradually blurs the boundary between realities and virtual, and experiential have a certain emotional investment in excessively real virtual space, to create a sense of immersion and to effectively cultivate students’ spatial thinking and higher-order thinking ability. A questionnaire on the effect of VR game teaching technology on college students’ immersion was designed, and the effects of four aspects of VR game teaching technology, namely publication rules, role description, game control, and game review on learners’ immersion were explored. Results show that publication rules, role descriptions, and game control in VR game teaching have significant positive effects on college students’ immersion. All samples of different game platforms show a significant degree of immersion (P<0.05), and two VR game platforms, namely wearable VR interaction and VR all-in-one, improve learners’ immersion more significantly. Results of this study have important reference value for integrating VR technology into three-dimensional composition teaching, stimulating students’ sense of participation in game learning, improving VR game teaching effect and improving learning efficiency
Success Factors to Promote Digital Learning Platforms: An Empirical Study From an Instructor’s Perspective
Get PDFDigital learning platforms have been used as teaching aids in many countries; however, their success factors are not well established in Thailand. The primary objective of this study was to identify and confirm the success factors to promote digital learning platforms in Thailand from instructors’ perspectives. The sample included 788 Thai instructors. The confirmatory factor analysis (CFA) method indicates a good fit of a four-factor model to the observed data. The CFA construct revealed four success factors in promoting digital learning platforms: education policy, online classroom management, online learning system, and training method. The results explain the influence each of the four factors. Finally, this paper proposes guidelines for the promotion of digital learning platforms.
 
Effects of Asynchronous Interaction on Positive Emotional Experiences of Learners during Online Learning
Get PDFAbstract—Online education is appreciated by an increasing number of people, and how to guarantee positive emotional experiences of learners during online learning becomes a core problem that has become the concern of education researchers. Online asynchronous interaction is an important communication method among learners during online learning. Owing to shortages and defects in time and space, interaction channel, and interaction content, online learning easily produces many “emotional blanks.” This research reviewed studies on online asynchronous interaction and emotional experiences at home and overseas. A total of 356 students majoring in Nursing from 5 universities in Henan Province were invited for the questionnaire survey. Influences of online asynchronous interaction, including people-to-system (P2S), people-to-people (P2P), and people-to-content (P2C), on the positive emotional experiences of learners were analyzed. Differences in positive emotional experiences under different weekly online asynchronous learning hours were estimated. Research results showed that Cronbach’s α of the questionnaire is 0.920, KMO value is 0.840, and the sphericity test significance is 0.000, indicating that the proposed questionnaire has very good reliability and validity. Evidently, P2S, P2P, and P2C can promote the positive emotional experiences of learners. Influences of weekly online asynchronous learning hours on frequency (F = 3.648, p = 0.006), intensity (F = 2.560, p = 0.038), and duration (F = 2.523, p = 0.041) of positive emotional experiences have statistical significance. Research results have important references in promoting online asynchronous interaction strategies, implementing specific interventions to online asynchronous interaction behaviors, and better providing convenient asynchronous online teaching services to students
Teaching Quality Monitoring and Evaluation in Higher Education through a Big Data Analysis
Get PDFIn the existing teaching quality monitoring and evaluation, there usually adopts only one method, which is relatively simple, lacking validation and verification in the analysis results. For this reason, this paper aims to conduct research on the teaching quality monitoring and evaluation in higher education based on big data analysis. Firstly, the teaching quality monitoring in higher education was made in five directions: teachers' teaching level, students' academic status, course learning effectiveness, students' competency, and students' employment status. Also, the time series forecasting model (Autoregressive Integrated Moving Average) and the differential equation model (GM(1,1)) model which can effectively predict the change trend of the series, are fused to make the predictive evaluation of the changes in data series of the higher education teaching quality. Next, a combined analysis was performed for both the teaching quality monitoring and evaluation results in higher education and the corresponding data on the frequency of proposing and promoting the improvement measures of teaching quality, and mathematical models were established through curve fitting and parameter estimation to explore the deep correlation between the two. Finally, the related experimental results were given to verify the fusion model. Therefore, the teaching quality monitoring and evaluation system in higher education based on big data analysis can realize the effective regulation of factors affecting teaching quality, and also provide convenience for the academic management of universities, which has certain research significance