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Measurement of Coupling Coordination Between Vocational Happiness and Affective Education Ability of Young Teachers
Get PDFWhen teachers are giving affective education, their emotional communication with students is a kind of state or process during which teachers’ work pressure can be alleviated when they are cultivating students’ sentiment, in the meantime, they also could attain positive mental quality and a sense of vocational happiness. Regarding the relationship between young teachers’ vocational happiness and their Affective Education Ability (AEA), only 1-2 articles have discussed it from the perspective of a single ability, there isn’t complete research on the AEA system of teachers. Therefore, to fill in this research blank, this paper aims to measure the coupling and coordination between the development of young teachers’ vocational happiness and their AEA, in the hopes of providing a piece of useful evidence for relevant research. The theory of emotional intelligence and positive psychology serve as the theoretical basis of this paper. In the second chapter, this paper builds a framework for young teachers’ affective education and an AEA training model, and employs Artificial Neural Network (ANN) to extract the time series data features of the divided six evaluation indexes of young teachers’ AEA. In the third chapter, this paper uses the Coupling Coordination Degree (CCD) to measure the coupling and coordination of the development level young teachers’ vocational happiness and their six kinds of AEA, and gives the quantification process. In the fourth chapter, this paper adopts the Grey Relational Analysis (GRA) to quantify the influence degree of all indexes on young teachers’ vocational happiness and their six kinds of AEA, thereby realizing the quantitative measurement of each evaluation index on the comprehensive development of the two. At last, experimental results are drawn to verify the measurement and analysis results of the coupling and coordination of the two
Factors Behind Virtual Assistance Usage Among iPhone Users: Theory of Reasoned Action
Get PDFSmartphone usage is increasing due to several features and opportunities offered by manufacturers and service providers. Existing literature attributes this increased usage to the availability of virtual assistants, mainly voice assistants, that ensure users' facility and maximum benefit. This research also focused on the factors behind voice assistant usage, particularly in iPhones among users in the United Arab Emirates. Notably, the conceptual framework in this research is supported by the Theory of Reasoned Action. The researchers adopted a cross-sectional design and gathered data from n= 251 respondents. Data analysis through Structural Equation Modelling revealed that, Perceived Ease of Use, Subjective Norms, and Perceived Behavioral Control work as significant factors contributing to iPhone-based voice assistant usage. On the other hand, Expectation Confirmation also works as a significant factor in Siri usage. On the other hand, Satisfaction significantly mediated (p> .022) the users’ Expectation Confirmation leading to Siri usage among the respondents. Further, the Artificial Neural Network Analysis (ANN) revealed an overall accuracy regarding Training at 14.8% and 30.8% concerning Testing. Thus, it is concluded that the devices accompanied by ease of use are preferred due to their distinguished services. In this regard, highlighting voice assistants' help also reflects the importance of technology in our daily lives. Especially mobile computing is playing a significant role in improving our everyday technology experiences
Influences of Problem-Based Online Learning on the Learning Outcomes of Learners
Get PDFAbstract—Influenced by the COVID-19 pandemic, online learning has become a major learning mode for most university students in China. For high-quality online teaching, teachers must optimize the teaching mode and strengthen the enthusiasm of students in classroom learning. In this study, a comparative experiment was carried out based on the course Cross-border E-commerce Customer Services for sophomore E-commerce majors at Huanghuai University in Zhumadian City, Henan Province, China. In the experiment, the teaching effect of the problem-based learning (PBL) mode in higher education was verified and gender differences in learning outcomes of learners who accepted PBL were analyzed. Results showed a significant difference at the 0.01 level between pre- and post-test results of the experimental group (t=−11.367, p=0.000), proving the teaching effect of PBL. The academic performances of the experimental class are significantly better compared with those of the control class, indicating that PBL is more beneficial to improve learning outcomes of students than traditional teaching methods. Gender has significant influences on post-test results at the 0.01 level (p=0.002<0.01). The median of final exam scores of male students is obviously higher than that of female students. Research conclusions can provide important references to test the effectiveness of PBL in learning outcomes of a specific subject, construct a PBL mode that can promote deep learning, and facilitate development of university teaching from knowledge teaching to core quality training of students
Information Dissemination Prediction of College Students’ Learning Requirements Based on Mobile Social Network
Get PDFStarting point of this research is the information dissemination and fluctuation of the learning requirements of college students between the student individuals and the mobile learning platforms, to solve this problem, it’s of certain practical value to explore the information dissemination efficiency of college students’ learning requirements under different conditions and study the prediction accuracy optimization method. However, existing studies generally focus on the feasibility of applying existing methods to the prediction of college students’ learning requirements, but the research on such prediction is neither sufficient nor deep enough. This paper constructed a model of college students’ mobile social network based on the actual data of college students’ mobile social behaviors, and innovatively proposed a novel algorithm for predicting college students’ requirements for mobile learning. At last, experimental results verified the effectiveness of the constructed model and the proposed algorithm
Digital Didactical Design: The Role of Learning Obstacles in Designing Combinatorics Digital Module for Vocational Students
Get PDFMany changes occur in the world of education as a result of technological advancement, including the compilation of a module. Digital modules that students can easily access and that have been compiled based on an analysis of student learning obstacles are required. Therefore, this research aims to develop a digital module based on student learning obstacles and it employs the theory of Digital Didactical Design (DDD). DDD contains components of learning objectives, learning activities, process-based assessment, social relations, and integrated technology. This is an on-going study relying on ADDIE (Analyze, Design, Development, Implement, Evaluation) model and it reveals the results from Analyze, Design, and Development stages. In the analysis stage, several instruments, such as test, interviews, and documentation, were used to encounter some learning obstacles in solving the combination problems faced by the vocational high school students. The next stage is designing the digital module based on the students' learning obstacles using DDD components. And in the development stage, the validity and practicality of the module are being tested and the results are very valid and practical. Thus, this digital module was feasible and can be used in teaching combination for vocational high school students to minimize learning obstacles
The Use of Personal Learning Environment to Support an Online Collaborative Strategy in Vocational Education Pedagogy Course
Get PDFOnline distance learning, on the one side, makes the learning process more efficient and flexible. However, on the other hand, it produces learning loss within a particular educational period and makes students less enthusiastic about building a collaborative attitude. In general, online learning platform providers have provided support for collaboration activity features, but in reality, they have not been able to support effective interaction. Experts believe that using the Personal Learning Environment (PLE) as an IT tool in the learning process can help create an effective online collaboration environment. This study found that PLE was proven to increase collaborative activities in online learning significantly. The reflection feature is the type of PLE that has the most significant influence on creating a good collaboration environment. The research also found that suitable collaborative activities can increase students' understanding of TVET pedagogy
Using New Technologies and Mobiles for Students with Disabilities to Build a Sustainable Inclusive Learning and Development Ecosystem
Get PDFNowadays, the educational policy, in many countries, promotes equal access for all students, including those with disabilities, to the general school, against all forms of social exclusion. Inclusion, in an innovative sense of the concept of diversity, focuses on the active participation and interaction of a heterogeneous student population in the general classroom. “E-inclusive” pedagogy refers to teachers’ decisions about the use of technology in the educational process with a view of compensating their developmental deficits and making functional use of their strengths. The aim of this paper is to propose tech tools and e-services for the access and active participation of students with sensory and motor disabilities in the educational procedures of the mainstream school and examine the role of teachers in realizing their inclusion / e-inclusion, as the main facilitators and modulators of the classroom settings to an open learning and development student-centered ecosystem. The results showed that teachers who accept as equal members of the school community all their students and incorporate flexibly new technologies into their teaching strategies to meet their unique needs, providing them with authentic opportunities for interaction and learning, contribute catalytically to their academic and social achievements, preparing them for substantial employment and integration opportunities in community life
A Comprehensive Systematic Review of Neural Networks and Their Impact on the Detection of Malicious Websites in Network Users
Get PDFThe large branches of Machine Learning represent an immense support for the detection of malicious websites, they can predict whether a URL is malicious or benign, leaving aside the cyber attacks that can generate for network users who are unaware of them. The objective of the research was to know the state of the art about Neural Networks and their impact for the Detection of malicious Websites in network users. For this purpose, a systematic literature review (SLR) was conducted from 2017 to 2021. The search identified 561 963 papers from different sources such as Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library and Microsoft Academic. Of the papers only 82 were considered based on exclusion criteria formulated by the author. As a result of the SLR, studies focused on machine learning (ML), where it recommends the use of algorithms to have a better and efficient prediction of malicious websites. For the researchers, this review presents a mapping of the findings on the most used machine learning techniques for malicious website detection, which are essential for a study because they increase the accuracy of an algorithm. It also shows the main machine learning methodologies that are used in the research papers
Mobile App Prototype: Telemedicine for Mental Health Care During Pandemic
Get PDFSince the end of 2019, the contagious disease COVID-19, which originated in the city of Wuhan, China; It affected every country in the world. As a measure to prevent the spread of the virus, the Peruvian government ordered quarantine and social distancing; As a consequence, people's economy and social life were affected, increasing stress, depression and anxiety, which are emotional disorders that affect mental health. Added to this is the lack of access to mental health services. The objective is to develop a prototype mobile telemedicine application for mental health services, based on Android. The methodology that was applied was the Rational Unified Process (RUP) methodology, because this methodology allows documenting, ordering and structuring software development. The result obtained was a telemedicine mobile application prototype with a design and functionalities that facilitate the access of patients and specialists to provide mental health services. The evaluation of the quality of the prototype was carried out through the judgment of experts, who evaluated the efficiency, usability and security of the application. Obtaining after the calculation, 4.66 average or mean; which indicates, according to the established quality range, that the quality of the mobile prototype is high. In conclusion, it was possible to develop an efficient, easy-to-use and safe mobile telemedicine prototype; that provides the solution by facilitating patients' access to mental health services for their emotional, psychological and social well-being
Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models
Get PDFThe emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%