Online Teaching Platform Research Articles

Effectiveness of online educational platforms in teaching English to medical students and staff: Experience and development prospects

Effectiveness of online educational platforms in teaching English to medical students and staff: Experience and development prospects

Building capacity and promoting health through online volunteering

Purpose Significant changes to in-person volunteering were necessary during the COVID-19 pandemic. Health Volunteers Overseas (HVO) responded to the pandemic by developing an online education platform and pivoting to online volunteering. This qualitative study explores the dynamics of online volunteering and provides visibility to this novel trend in the training of health professionals in resource-scarce countries. Methodology/approach Barriers and facilitators, actual and potential outcomes, and recommendations for online volunteering were addressed through a comparative case study approach focusing on three HVO global projects: physical therapy in Rwanda and Ghana, oral health in Nepal, and anesthesiology—a multi-country collaboration between Bhutan, Cambodia, and Laos. Findings A total of 15 participants, including project directors, host country representatives, and teaching volunteers, were interviewed online. Three themes emerged through the online interviews with HVO volunteers, project directors, and host country directors: Creating Space, the Future is Hybrid, and Geographical Expansion. Research implications Online volunteering provides a new opportunity for volunteers to expand their networks, learn new skills, and meet the needs of the host country participants. There is also an opportunity to expand projects beyond national boundaries, but logistical, cultural, and linguistic challenges must be considered. Practical implications Online volunteering is a feasible approach for building teaching and research capacity globally, and volunteer agencies should consider a hybrid model combining online volunteering with in-person volunteering. Originality Online volunteering provides support to meet the needs of both volunteers and in-country program participants. Limitations This study focused on the volunteer, project director, and host coordinator experience and not on the learner. More research is needed to investigate how online and in-person dynamics coalesce.

CRITERIA FOR EVALUATING AN E-LEARNING EDUCATIONAL PLATFORM FOR TRAINING COMPETITIVE PROFESSIONALS

This study explores the differences between the terms "online educational platform" and "electronic educational platform," emphasizing their interconnections and distinctions within the context of contemporary education. By analyzing various aspects of both platforms, their impact on the learning process is highlighted. Special attention is given to the importance of evaluation factors for digital educational platforms. The possibilities for optimizing the learning environment to meet the needs of modern society are identified.

Learning management systems for higher education: a brief comparison

The learning management system (LMS) is a software based on SAAS, internet navigator, or user application, which manage the teaching and learning of an academic or non-academic program. This work presents a comparison of 45 LMSs. The objective of this research is to report a study of the LMS developed for higher education. This objective is addressed by considering all LMSs with online access to information. For the comparison study, an evaluation criteria has been developed based on the literature review from the past 20 years. This evaluation methodology is based on Software Quality and Teaching-Learning tools for online educational platforms (SQTL), it is composed by six criteria: interoperability, accessibility, productivity tools, communication tools, learning tools, finally with the security standards and certifications. The maximum sum of SQTL score is 10 points and the minimum 0. The best LMS according to the comparison are Paradiso and Moodle with 9.50 and 9.25 points, respectively. Paradiso has around 1 million users and Moodle over 250 millions. The Google Sheets with the collected raw data and SQTL scores are available in https://shorturl.at/wqDWk.

Track and Field Teaching Based on Computer Network Resources

Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students’ interest data, so as to find out the rules and patterns of users’ access to instructional resources, thus further optimizing the allocation of users’ access to instructional resources, improving the efficiency of users’ access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm.

Emergency remote teaching amid global distress: how did teacher educators respond, cope, and plan for recovery?

This study explored the emergency remote teaching experiences of Ghanaian teacher educators during COVID-19. The study employed a basic qualitative interpretive approach and purposively interviewed 25 teacher educators from 15 teacher training institutions. Teacher educators in this study reported that emergency remote teaching (ERT) was a learning opportunity and professional capacity-building experience for them to engage in contemporary pedagogical practices. Teachers also indicated that synchronous and asynchronous remote teaching was helpful for their students, as it provided students with the opportunity to engage in self-paced learning due to their access to learning resources at any time. ERT promoted peer teaching, team teaching, and effective collaboration among teacher educators. However, with little preparation and training for remote teaching and learning, both teachers and students struggled with the remote teaching and learning process. A myriad of challenges were identified including the unsuitability and unfamiliarity of online teaching and learning platforms, a high rate of absenteeism and low student engagement, a lack of parental and school support, and inadequate technological resources. The study revealed that mathematics and science teachers needed advanced technological resources to support student learning. Implications for educational policy and practice are discussed.

College peer effects on learning behaviors in synchronous online courses

This study contributes to the growing body of research on peer effects in educational contexts by examining the peer effects on learning behaviors, which are pivotal in understanding how peers impact academic achievements, especially in online education. Drawing upon an administrative dataset and a learning behavior dataset from an online education platform at a Chinese research university in 2020, we employ fixed effects and dynamic models to investigate the peer effects on undergraduates' class absence. Notably, we further consider partially overlapping networks of peers and utilize the class absence of “classmates of classmates” (who are not one's own classmates) as instrumental variables for peers' absence to address potential estimation biases. Our findings reveal significantly positive effects of classmates' class absence on individuals' both full-class and part-class absence in synchronous online courses. Additionally, heterogeneity analyses show that males, freshmen and sophomore students are more susceptible to peer influence and exhibit greater influence on their peers, and peer effects are stronger in subject required courses, courses with larger class sizes, courses with mandatory attendance policies, and among students in the same department or grade level.

A Study on the Factors Influencing the Sustainable Development of Education in the Context of COVID-19: Tencent Conference Online Platform

The difficulties of offline education caused by the COVID-19 epidemic are attracting increasing public attention. Although international research on online education platforms is gradually deepening, the research on the influencing factors of Chinese users’ participation in these platforms shows different results due to regional differences. Therefore, this study adopts the technology acceptance model (TAM) as the basis to build the index system of influencing factors for the Tencent conference online education platform. The questionnaire design covered five dimensions, including external environment, platform satisfaction, and continuous use intention. An online questionnaire survey was conducted on participants in some regions of China, 320 questionnaires were distributed, and 297 valid questionnaires were collected. The data were analyzed by SPSS 26.0, and the results of Cronbach’s coefficient of 0.939 and a KMO value of 0.941 were obtained. The weight value, entropy value, and importance ranking of the factors were determined by combining hierarchical analysis and the entropy weighting method. First, the perceived ease of use is the most critical factor influencing the users’ use of online delivery platforms. Second, freedom from geographical time difference, the ease of operation, system perfection, and proficiency in use greatly influenced the participants’ use of the online lecture platform. Third, the study group suggested that the platform should be optimized in terms of convenience, stability, and freedom from geographical time difference, and provide reasonable references and lessons for future educational efforts.

Potential factors-embedding group recommendation for online education

Online education platform urgently needs recommendation methods to service learning groups. The existing group recommendation methods rely on member preference aggregation. However, potential factors such as teachers and schools have an important impact on the decision-making of learning groups. Due to the different ways in which potential factors operate, it is very challenging to model the impact these potential factors have on groups. In this paper, we propose a group recommendation method for online education (GEMst) that innovatively designs two modules to capture different types of potential factor embedding. In the subjective guidance module, we construct a heterogeneous information network based on the relationship between guiders (teacher, leader, etc.) and groups, analyze the influence of the guider on the group, and obtain the group representation from this perspective. In the objective environment hypergraph module, we consider the influence mode of the environment (school, platform, etc.) on the group, and propose the objective relationship hypergraph convolution. Compared with hypergraph convolution, it directly learns the influence between groups in the same environment. We provide information fusion strategies that GEMst can collaboratively consider group members’ opinions and the impact of potential factors. In addition, to enhance the learning effect of the model, we design a pre-training strategy based on the user grouping relationship that obtains more accurate user embedding representations. We test GEMst on two real-world datasets, and the results show that GEMst outperforms the current baseline group recommendation method.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

Development of an online teaching platform to improve access to postgraduate pathology training in sub-Saharan Africa.

Resource barriers to the provision of accessible training in cancer diagnosis in lower- and middle-income countries (LMICs) limit the potential of African health systems. Long-term provision via teaching visits from senior pathologists and trainee foreign placements is unsustainable due to the prohibitive costs of travel and subsistence. Emerging eLearning methods would allow pathologists to be trained by experts in a cheaper, more efficient, and more scalable way. This study aimed to develop an online teaching platform, starting with hematopathology, for trainee pathologists in sub-Saharan Africa, initially in Nairobi, Kenya, and Lusaka, Zambia. Course materials were prepared for both Canvas and the Zoom eLearning platforms using digitally scanned slides of lymph nodes and bone marrow trephines. Initial in-person visits were made to each site to establish trainee rapport and maximize engagement, evaluate different methods and course content, and obtain feedback to develop the project. The knowledge of trainees before and after course completion was used to measure initial effectiveness. Online teaching with the preferred platform is to be continued for 1 year before re-evaluation for long-term effectiveness. Canvas was selected as the preferred delivery platform as it is freely available and has good functionality to support all required tasks. Face-to-face teaching was considered optimal to establish the initial rapport necessary to maximize subsequent engagement with online teaching. Challenges have included sub-optimal internet speeds and connections and scheduling issues. Weekly online hematopathology teaching sessions using live image capture microscope sessions, Zoom, and Canvas have been delivered to students in Kenya and Zambia, with good attendance and interaction in case discussions. Our team has successfully designed and delivered an online training program in hematopathology to trainee pathologists in Kenya and Zambia, which has been ongoing for over a year. This project is now being scaled to other sub-Saharan countries and other sub-specialties.

Breaking Barriers: Women's Empowerment Through Online Education Platforms

Breaking Barriers: Women's Empowerment Through Online Education Platforms

A meta-analysis of learners’ continuance intention toward online education platforms

A meta-analysis of learners’ continuance intention toward online education platforms

Design Of An Efficient Model For Enhancing Online Teaching Platform Adoption Among Teachers During Pandemics

Design Of An Efficient Model For Enhancing Online Teaching Platform Adoption Among Teachers During Pandemics

Speech emotion recognition using a combination of variational mode decomposition and Hilbert transform

The objective of automated speech emotion recognition (SER) is to recognize each emotion of the speech signal uniquely and efficiently using machines like computers, mobile devices, etc. The popularity of SER has been widely acknowledged among researchers due to its extensive applicability in practical contexts. The use of SER has shown to be advantageous in several domains, including medical treatment, enhancement of security systems, surveillance operations, online marketing strategies, online educational platforms, online search engines, personal communication, customer relationship management, reinforcement of machine and human connection, and numerous other areas. Numerous authors have used various techniques, including the combination of multiple features (acoustic, non-acoustic, or both) and classifiers (machine learning, deep learning, or both), in order to enhance the efficacy of emotion categorization. In the present work, our objective is to enhance the performance of emotion classification (PECL) by using a combined approach of the variational mode decomposition (VMD) and Hilbert transform (HT) techniques. Using the VMD method, we decomposed the speech signal frame into many sub-signals, or intrinsic mode functions (IMFs). Then HT is applied to each VMD-based IMF signal to find the mode instantaneous amplitude (MIA) and mode instantaneous frequency (MIF) signal vectors. We extracted proposed features such as HT-based approximate entropy (HTAE), HT-based permutation entropy (HTPE), HT-based increment entropy (HTIE), and HT-based sample entropy (HTSE) using each MIA and MIF signal vector. The combination of the proposed HT-based features is called HT-based entropy (HTE) features. Then, we accessed the PECL using the HTE and MFCC features alone and in conjunction with a deep neural network (DNN) classifier. The experiment's results showed that the combinations of the proposed feature (MFCC + HTE) using a DNN classifier outperformed the individual features and obtained a SER accuracy of 86.92% for the EMOVO dataset and 91.63% for the EMO-DB dataset.

한⋅베 식문화 비교를 통한 사회통합프로그램 교재 개선 방안 연구

Objectives The purpose of this study is to compare the food cultures of Korea and Vietnam and to explore ways to supplement the content of food culture education in the textbooks ‘Korean Language and Culture’ Beginner 1⋅ 2 and Intermediate 1⋅2, which are used in the Korea Immigration and Integration Program. Methods This study compared the food cultures of Korea and Vietnam through a literature review and analyzed the content of food culture education in the ‘Korean Language and Culture’ textbooks Beginner 1⋅2 and Intermediate 1⋅2. Based on this analysis, it identified areas of deficiency in the textbooks and proposed educational strategies to enhance learning effectiveness. Results The comparison of food cultures between Korea and Vietnam revealed significant differences in food styles, cooking methods, and dining customs between the two countries. Additionally, it was identified that there are areas in the food culture education content of the ‘Korean Language and Culture’ textbooks that require improvement. Conclusions In this study, strategies and methods for educating Vietnamese female marriage immigrants on food culture through the ‘Korean Language and Culture’ textbook were proposed. By expanding and enhancing the content of the textbook, not only can the effectiveness of learning be increased, but also adaptability can be enhanced by introducing practical experience programs where learners can directly experience and learn about Korean dining culture. In the future, more systematic development of educational materials and expansion of practical experience programs will be necessary, and conducting comparative studies on various cultures through online education platforms will further enhance the effectiveness of food culture education.

SPOC 双线融合教学设计与实践

Online teaching platforms and technologies have become important means of offline teaching. The two teaching scenarios should be fully integrated and collaborate to complete teaching tasks. Following the concept of collaborative teaching and the integration of teaching elements by Josh Bersin, this paper reconstructs and designs course teaching content through four stages: learner demand feature recognition, learning plan formulation, learning resource development, and learning plan execution. Three integration methods, including flipping, flexibility, and complementary classrooms, are introduced to complete teaching practice. The assessment results of final close-book paper test and the satisfaction evaluation of integrated teaching give affirmation to the teaching effectiveness. Therefore, it can be considered that teachers should choose different dual line integrated teaching models based on the differences in course content to drive learning pace and improve classroom teaching efficiency.

Liaoning University ChopMelon Net Project: Innovative Research on Sustainable Education Based on Real Social Issues

The purpose of this study is to evaluate how the ChopMelon Net online learning platform can contribute to the effectiveness of sustainable education by incorporating real social issues. The core innovation of ChopMelon Net is that it provides a learning environment that connects learners directly to real-world challenges and aims to enhance learners’ understanding of sustainable development theory. The study employed the technology acceptance model (TAM), theory of planned behavior (TPB), and knowledge–Behavior gap theory (KBGT) to design the questionnaires and semi-structured interviews in order to comprehensively assess the impact of ChopMelon Net on learners’ skill enhancement. The results showed that learners not only showed significant improvement in critical thinking and problem-solving skills after using ChopMelon Net, but also expressed a high level of satisfaction with the educational content and interactive experience provided by the platform. In addition, the study found that ChopMelon Net effectively promoted learners’ awareness of the importance of sustainable education and behavioral change through its innovative educational model, which provides important empirical support for future sustainable education practices and the design of online education platforms. This study highlights the importance and effectiveness of incorporating real social issues into the teaching and learning process, with far-reaching implications for sustainable innovation in education.

Using an Online Teaching Platform to Improve Corrective Feedback Practices in EFL Writing Lessons

Distance education has become a prominent part of the education system around the globe in recent years. The limitations and problems encountered during online lessons have made teachers and administrators overlook the many benefits that distance education can offer. In an attempt to dispel this misconception, this study shows that not only can online lessons match the efficiency of traditional face-to-face lessons, but they can actually become more efficient in teaching writing to EFL students. Sixteen undergraduate students from a state university received online EFL writing lessons for two months using Google Classroom. A questionnaire was created to investigate the benefits of using this online platform in such lessons. The results were interpreted by calculating the average means for each questionnaire section and examining participant answers to open-ended questions. Overall, the participants declared that the use of Google Classroom improved both the quality and quantity of feedback, interaction, and group work in online lessons, resulting in better learning outcomes.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.