Online Self-learning Research Articles

Applying the technology acceptance model to online self-learning: A multigroup analysis

This study focuses on the structure of self-learning as a multidimensional construct, through competences such as autonomy and study planning of undergraduate students, over the last three years. The objective of this study, framed by Technology Acceptance Model (TAM), aims to gain insights into the acceptance of self-learning in online educational models within a post-pandemic, university context. More specifically and innovatively, this study sets out to explore gender differences in self-learning adoption, as previous studies have observed various findings.Action variables and motivation play crucial roles in self-learning. Davis's original TAM model (1989) put forward five key variables, all of which are dealt with both theoretically and practically in this study. Additionally, this study puts forward several hypotheses based on the TAM model, addressing perceived ease of use, perceived usefulness, attitude, user satisfaction and intention to use in the context of online learning.From a practical perspective, a multi-group analysis is carried out that has been applied to a sample made up of 313 male and female Spanish university students from different areas of knowledge. Questionnaires were conducted with all participants and statistics calculated; the results make it possible to affirm that the different dimensions of the self-learning structure are interrelated, and significant differences between men and women are observed in the adoption of the model.The study concludes that online education fosters a positive attitude toward self-learning in female students, unlike what happens with male students. While the sample size might be considered a limitation of the study, the study nonetheless observes that female students appear to be more inclined to develop a sense of self-learning. In contrast, male students—though consumers of self-learning– do not generate the same attitude. Therefore, the conclusion can be drawn that online teaching is a way to promote self-learning in women, which may inform universities when designing programs promoting student autonomy with new technologies.

Temporal online self-learning stochastic configuration networks: A study on strip deviation prediction

Stochastic configuration networks (SCNs), with the rapid learning abilities and universal approximation properties, have garnered growing interest in the industrial domain in recent years. In the hot rolling process, strip deviation affects product quality and production safety. In this paper, a novel model named temporal online self-learning stochastic configuration networks (TOSL-SCNs) designed for early prediction of strip deviation in the hot rolling process is introduced. To address the challenges of time series prediction, conventional SCNs are extended to a two-dimensional format, enabling effective processing of time series data. Given the dynamic nature of industrial environments, the model incorporates an online self-learning capability to adapt to nonstationary data. Additionally, to ensure the stability of the model’s predictive performance, a data buffer strategy is introduced to dynamically refresh the training dataset. This paper demonstrates the capabilities of TOSL-SCNs in time series modeling, online self-learning, and data buffer strategy from multiple perspectives. The case study on strip deviation prediction highlights the necessity of these capabilities in industrial applications.

Higher Education in China during the Pandemic: Analyzing Online Self-Learning Motivation Using Bayesian Networks

The COVID-19 pandemic has led to an unprecedented shift towards online learning, compelling university students worldwide to engage in self-directed learning within remote environments. Despite the increasing importance of online education, the factors driving students’ motivation for self-directed online learning, particularly those involving economic incentives, have not been thoroughly explored. This study aims to address this gap by analyzing large-scale data collected from 19,023 university students across China during the pandemic. Using mixed Bayesian networks and multigroup structural equation modeling, the study explores the complex relationships between personal characteristics, academic characteristics, the academic environment, and students’ motivation for self-directed online learning. The results reveal significant associations between online self-directed learning motivation and personal characteristics. such as gender and age, academic characteristics, such as education level and learning incentives, and the geographic location of the school within the academic environment. Moreover, the causal relationship between school location and online self-directed learning motivation varies by gender and educational level. This research not only provides new empirical support for the theoretical framework of online learning motivation but also contributes to the broader fields of educational psychology and online learning research.

Finding alternative community-based learning delivery for parenting skills during COVID-19 for mothers with children aged 0–3 Years

Parenting training has been considered crucial to improving child development. The Tanoto Foundation's parenting program intervention was started in 2021 when the COVID-19 pandemic impacted government policies in Indonesia. Preventive measures that limit mobility affect the sustainability of face-to-face parenting interventions. The study aimed to explore alternative intervention methods, such as face-to-face, blended, online with facilitators, and self-learning, for parenting skills learning during emergencies. A 1-year non-randomized quasi-field experiment using a mixed quantitative-qualitative approach was conducted to 762 participants. SIGAP Q, the HOME Inventory, and CREDI were used for quantitative measurements, while interviews and focused group discussions (FGDs) provided qualitative data. The quantitative data were analyzed using multiway ANOVA, and the qualitative data were analyzed using thematic analysis. The study found that all intervention modalities delivered positive outcomes. In contrast, face-to-face delivered the largest gain, followed by online with facilitators, blended learning, and online self-learning (a web-based learning management system). As an alternative, online with facilitators is the best for delivering parenting materials, followed by online self-learning (independent) modes of intervention. Blended and online models provide alternative models in emergency contexts. Implications are discussed in this article.

Improving students’ problem-solving abilities through answering questions in metaverse games: taking GOXR software as an example

In computer-based dialogue learning, students’ willingness to communicate and ability to interact is one of the important factors. Few studies have examined the testing of mobile metaverse teaching material systems built for interactive communication and learning courses. This study developed and designed course content for a virtual classroom, which is suitable for mobile learning on Android or iOS systems and can provide metaverse space for students to discuss homework using their mobile phones, as well as a system for online self-learning and testing. Students can participate in classes without time or space restrictions. In addition, questions related to life math knowledge and leveling up are included, such as the basic reproduction number of COVID-19, influenza, and enterovirus. There were 100 valid responses from two schools, which recognized the convenience and benefit of the virtual digital technology of the mobile self-learning system in the metaverse space as an auxiliary tool for learning, as it provides a more flexible and diversified educational learning mode. This system can be used for course teaching, after-class review, and testing to facilitate self-testing of learning results.

Current Status of Online Self-Learning Efficacy Level for High School Students

Self-efficacy in learning is an individual's belief in their ability to effectively achieve learning goals during autonomous learning. Its measurement is typically conducted through self-report questionnaires, aiming to comprehensively assess students' levels of self-efficacy in learning. Factors influencing self-efficacy in learning include personal successful experiences, personality traits, social support, learning environments, task difficulty, among others. Self-efficacy in learning is closely related to online learning, as it not only affects students' attitudes, motivation, and strategies in learning but also predicts their behaviors and outcomes in online learning. Therefore, enhancing students' self-efficacy in learning is crucial for promoting their autonomous learning and academic success in online learning environments.

Application of self-learning interval type-2 fuzzy neural network in PM2.5 concentration prediction

Abstract The change of PM2.5 concentration in air quality is nonlinear and difficult to predict. Therefore, a self-learning interval type-2 fuzzy neural network (SLIT2FNN) is proposed. SLIT2FNN has two parts: online structure learning and parameter learning. In structure learning, to improve the training accuracy and speed of the model, the Possibilistic Fuzzy C-Means (PFCM) algorithm is used to process the input data and obtain the number of initial rules before model training. The PFCM algorithm introduces the concept of possibility P to Fuzzy C-Means (FCM), allowing PFCM to overcome the shortcomings of FCM that cannot accurately cluster a large number of nonlinear problems. SLIT2FNN can establish an appropriate number of rules in the preparation stage, and then use the firing strength of the antecedents of the rules to judge whether to generate fuzzy rules for online self-learning, thereby optimizing its network structure. Then, the improved Levenberg–Marquardt (ILM) algorithm is used to modify the relevant parameters of SLIT2FNN. The ILM algorithm can address the challenge of numerous parameters in the Jacobian matrix and complex calculations and improve the calculation speed and adaptive ability of SLIT2FNN parameter learning. Finally, SLIT2FNN is applied to the prediction of air quality PM2.5 concentration, and the performance is compared with other models. The experiment proves that SLIT2FNN has a high prediction accuracy and fast convergence.

Internet + Badminton Training in the New Context

Badminton is popular among people of all ages, and it also occupies an important position in the Chinese badminton market. As a global sports activity, badminton has received extensive attention in recent years at home and abroad. With the rapid development of Internet technology, all walks of life are actively exploring the integration of the Internet and traditional industries. In this context, the badminton training industry has also ushered in new opportunities and challenges. Traditional badminton training mainly relies on training venues and coaches for badminton. And now online training platforms provide more possibilities for it. With the gradual improvement of living standards, more and more parents attach more importance to the comprehensive development of their children, and interest training institutions for teenagers and children have mushroomed. However, from the perspective of the interest training market for teenagers and children, there are few sports trainings, most of which have no educational qualifications, and the cost is expensive. In this context, Internet plus+badminton training will have unique advantages, we can provide a platform for physical exercise for teenagers aged 6-16 through online self-learning, self-training, and offline small class tutoring.

Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Analysis of Junior High School Social Studies Teachers' Strategies in Facing the Demands of the Society 5.0 Curriculum Era (A Study in the City of Banda Aceh)

This research aims to describe the implementation of junior high school social studies (IPS) learning in the era of Society 5.0 in Banda Aceh City in facing curriculum demands, analyze the strategies of junior high school IPS teachers in enhancing their own competencies, and describe the solutions that junior high school IPS teachers employ to overcome challenges in facing the curriculum demands of the Society 5.0 era. This research utilizes a qualitative approach with a case study design. Data collection techniques include interviews and documentation. Data analysis techniques include data reduction, data presentation, verification, and drawing conclusions. The results of the research indicate that the models and methods of IPS learning in junior high schools in Banda Aceh City during the Society 5.0 era demonstrate creativity by implementing various learning models and integrating technology, such as discovery learning, project-based learning (PBL), and discussions using media such as Canva, picture and picture, and employing a differentiation approach, among others. Junior high school IPS teachers enhance their competencies through activities such as MGMP (Teacher Subject Study Group), FORGIPSI (Forum Guru IPS Indonesia), and online self-learning, focusing on collaboration, technology integration, and the development of critical thinking skills. Solutions that junior high school IPS teachers can take include active participation in training activities, webinars, seminars, and collaboration among teachers. Despite limitations in IT facilities, teachers continue to make efforts to create an effective learning environment while motivating themselves to adapt to the future development of students.

Online Learning and Its Effects on Students’ Writing Skills

As a side effect of the emergency transition to remote online learning, all schools’ students and universities have stopped teaching in classroom settings in mid-March 2020 and began using internet platforms to deliver online learning activities. Teachers and students communicate through chat, video calls, or emails. The government also launched educational television programming as an alternative for students who do not have access to the internet. Online learning is becoming more popular. However, there are problems with students' perseverance and achievement. Many learners can't study well in difficult situations like a pandemic. So, we need to think about learning strategies that give all students chances to achieve specific goals when they're studying at home. Writing in a foreign language is hard. This study looks at how online self-learning helps students. It checks their writing and they adjust their learning. They learn to write better. The aim of this study is to show how online learning enhances writing skills.

Imitation learning enabled fast and adaptive task scheduling in cloud

Studies of resource provision in cloud computing have drawn extensive attention, since effective task scheduling solutions promise an energy-efficient way of utilizing resources while meeting diverse requirements of users. Deep reinforcement learning (DRL) has demonstrated its outstanding capability in tackling this issue with the ability of online self-learning, however, it is still prevented by the low sampling efficiency, poor sample validity, and slow convergence speed especially for deadline constrained applications. To address these challenges, an Imitation Learning Enabled Fast and Adaptive Task Scheduling (ILETS) framework based on DRL is proposed in this paper. First, we introduce behavior cloning to provide a well-behaved and robust model through Offline Initial Network Parameters Training (OINPT) so as to guarantee the initial decision-making quality of DRL. Next, we design a novel Online Asynchronous Imitation Learning (OAIL)-based method to assist the DRL agent to re-optimize its policy and to against the oscillations caused by the high dynamic of the cloud, which promises DRL agent moving towards the optimal policy with a fast and stable process. Extensive experiments on the real-world dataset have demonstrated that the proposed ILETS can consistently produce shorter response time, lower energy consumption and higher success rate than the baselines and other state-of-the-art methods at the accelerated convergence speed.

Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn “good enough” subproblem solutions of deterministic UC. Compared to original UC, a subproblem is much easier to learn. Nevertheless, predicting good-enough subproblem solutions is still challenging because of the “jumps” of binary decisions and many types of constraints. To overcome these issues, subproblem dimensionality is reduced via aggregating multipliers. Multiplier distributions are novelly specified based on “jumps” for effective learning. Loss functions are innovatively designed to improve prediction qualities. Ordinal Optimization and branch-and-cut are used as backups for unfamiliar cases. Furthermore, online self-learning is seamlessly integrated with offline learning to exploit solutions from daily operations. Results on the IEEE 118-bus system and the Polish 2383-bus system demonstrate that continual learning keeps on improving the subproblem-solving process with near-optimality of the overall solutions maintained. Our method opens a new direction to solve complicated UC.

Nursing Undergraduate Students' Experiences and Perceptions of Blended Learning in Pediatric Nursing: A Mixed Methods Study.

Blended learning is a teaching approach that integrates online self-learning and classroom teaching. It emphasizes students' self-directed learning, actively engaging, as well as communication and collaboration. Blended learning develops the qualities in nursing undergraduate students with problem-solving ability, clinical thinking, and communication skills as nurses have. Currently, learners' experience has become an important factor in determining the success of blended learning. To learn about the experiences and perceptions of undergraduate nursing students regarding blending learning in pediatric nursing. A mixed methods design beginning with a quantitative data collection and analysis phase to inform the subsequent qualitative phase was utilized. This study was conducted among undergraduate nursing students at one university in China. In the quantitative study phase, a convenience sample of 59 students completed the online survey while the qualitative study phase included nine students recruited by purposive sampling. The qualitative data were collected using individual semistructured in-depth interviews. Audio data were transcribed and subjected to thematic analysis. The findings from those two phases were integrated into the final analysis. The majority (89.8%) of students reported satisfaction with blended learning. Among various teaching methods, case-based learning (CBL) was the most preferred (98.3%). Regarding the qualitative data, students reported positive experiences with blended learning including well-organized course structure, resourceful course content, and high teacher quality. They stated that blended learning improved the learning outcomes such as improving problem-solving ability and developing clinical thinking. They also provided suggestions for achieving better learning outcomes, including providing stressful learning activities, optimizing online course design, and improving the quality of group learning. The blended learning model of pediatric nursing based on the Community of Inquiry framework provides a positive learning experience for nursing undergraduate students. However, ongoing assessment and improvement of the blended learning process are also needed.

An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars

An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars

Factors affecting online self-learning acceptance among older adults after the pandemic: living arrangements and gender as mediators

ABSTRACT Research on acceptance of online self-learning is regularly based on the technology acceptance model (TAM), but less has been discussed on older adults. This article expands TAM by taking living arrangements and gender as moderators after the pandemic. Utilizing a structural equation model based on the partial least squares technique to examine how living arrangements and gender differences influence the acceptance of online self-learning among older adults above 55 in China. Findings show that male older adults favor status and value orientations, so their self-learning intention was significantly affected by perceived usefulness and social influence. Female ones prefer social and utilitarian orientations; therefore and their online self-learning behavior was affected by perceived usefulness, perceived ease to use, social influence, trust, and perceive financial cost. Types of living arrangements moderated TAM significantly among older adults. This article deepens the theoretical discussion and cognition of the TAM model in the online self-learning of older adults.

Urological Online Learning through WeChat in China: Lessons from the Covid-19 Pandemic

A public platform named Jiujing for the urologists’ online learning on the most popular social media WeChat in China was founded in 2015. Backend database from Jiujing before and during Covid-19 pandemic were compared and analyzed. An online questionnaire investigation containing 482 respondents was carried out to evaluate the personal characteristics and online learning preferences of Chinese urologists. During the epidemic of Covid-19, the amounts of national subscribers in Jiujing platform increased by 26.24%, while the constituent ratio of subscribers’ quantity from Hubei province decreased although there were 189 new subscribers, and only surgery video section experienced increased constituent ratio of subscribers’ concerns. Most respondents experienced whittled offline learning opportunities and boosted online learning demands during Covid-19 pandemic as online self-learning was more efficiency. Surgery videos and academic lectures were the most popular sections. Urologists’ viewpoints towards online learning are associated with professional post, affiliation hierarchy and education background.

The Impact of COVID-19 Pandemic on Flipped Classroom for EFL Courses: A Systematic Literature Review.

The outbreak of the COVID-19 pandemic has had a significant impact on education. The closure of schools and the cessation of face-to-face classrooms have affected schools and students worldwide. The current need is to transform the traditional classroom to adapt to the new social and educational background. The flipped classroom is usually defined as a strategy to subvert the conventional academic environment; that is, the information transmission part of the traditional face-to-face lecture is removed from the classroom time for online self-learning. The flipped classroom is a highly flexible classroom mode, which has brought significant changes to education. Therefore, this study aims to examine the studies' research trends, advantages, and challenges concerning the flipped classroom for EFL courses during the COVID-19 epidemic. For this purpose, databases including the web of Science (WOS) and Scopus were reviewed, and 15 articles were analyzed. A systematic review was used as the research methodology. The study's findings revealed the effectiveness of flipped classrooms for EFL courses during the pandemic. Based on the review, this paper puts forward suggestions for future research and points out the future development direction.

Evaluation of online learning platforms based on probabilistic linguistic term sets with self-confidence multiple attribute group decision making method

Online education addresses the limitations of traditional classrooms in terms of time and location. Moreover, online learning platforms provide learners with one-stop self-learning services. The organic combination of online self-learning, traditional classroom teaching and classroom experiment is a persistent task in realizing the development of diversified teaching modes in colleges and universities. Thus, selecting an appropriate online learning platform for students to improve ability and quality can be viewed as a complicated multiple attribute group decision making (MAGDM) problem. This study aims to introduce a new method for evaluation of online learning platforms based on MAGDM. First, the evaluation index system of online learning platforms is constructed. The index contains three dimensions and six indicators based on the ‘1 + 4′ multi-dimensional Cooperation-Acknowledgement-Modulation-Perseverance (CAMP) ability training systems. On the basis of assessment information using probabilistic linguistic term sets with self-confidence (PLTS-SC), we then introduce certain basic operational laws and aggregation operators for PLTS-SC. To aggregate individual evaluation information into a collective result, we further propose a weight determination method of experts that combines levels of external trust with levels of internal self-confidence. Afterward, we put forward an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for MAGDM using the PLTS-SC data and apply it to the evaluation and selection of online learning platforms. Finally, the feasibility and effectiveness of the proposed method is verified by robustness test and comparative analysis with relative methods.

Students' readiness to online English language self-learning module and approaches to its integration into the educational process

Introduction. The transition to blended learning in higher education has led to an increase in the share of self-learning and personalized education, which demands taking into account the students' opinions about the variety of teaching methods. The purpose of the article is to analyze the students’ readiness to the integration of the “Online Self-Learning Module”, when learning English and to its implementation approaches. Materials and methods. The main research method is a survey of the 2-3-year students of non-linguistic majors conducted at Togliatti State University (September – November 2021). The questionnaire consisted of 15 questions in three blocks: “Personal goals for self-learning”, “Self-directed learning skills” and “Monitoring of online self-learning process/ Feedback”. Survey results reliability evaluation was carried out using Fisher criterion (φ*-criterion) (p < 0,01). The results. The survey revealed a positive attitude of students to its integration: desire of extra curriculum English training (77%) φ*exp. = 8.068; need for skills of professional texts independent study (65%) φ*exp. = 4.306, personal pace and achievements (67%) φ*exp. = 4.907 Weaknesses, according to the students, include: nonawareness of the teacher-students interaction algorithm (82%) φ*exp. = 9.822; the need for self-assessment and reflection (68%) φ*exp. = 5.204. We determined a new approach to students' self-learning: extra curriculum online task with its modular approach, structurally and thematically correlated with offline learning; the availability of the students’ guidelines and the tasks fulfillment algorithms, the sample for the final answer, and automated evaluation of the assignment assessment. Conclusion. Students identified the “Online Self-Learning Module” for English as a positive trend that will allow them their personalized English learning, to be autonomous in choosing language content, not to depend on the teacher’s direct control, and to increase their self-organization and self-reflection. Integration of “Online Self-Learning Module” involves the educational technologies modernization and serves as a response to the current challenge of the education.