Online Teaching Platform Research Articles

Adaptation Of Socrative Application As Online Teaching Platform During The Covid-19 Pandemic

Purpose: This study aimed to investigate university students' attitude towards Socrative as an online teaching tool and a student smart response system (SRS) in real-time during the Covid-19 school closure at Walailak University (WU). The COVID-19 pandemic has prevented WU lecturers from engaging in face-to-face learning. Socrative, Microsoft Team, and other e-learning programs were among the various remote teaching applications that were used during this period. However, because of the alleged high levels of reliability and effectiveness, the researcher concentrated on a proprietary e-learning application, Socrative. Method: This study used a mixed method of quantitative and qualitative research design to investigate the sampled population of (n=90) second-year undergraduate students’ attitudes toward using Socrative as an online teaching tool and real-time feedback after having used the aforementioned application for three months during the COVID-19) pandemic. Results: The findings revealed that Socrative is indeed a useful tool that educators can use to improve digital learning outcomes such as learning engagement. The results also revealed that gender did not have any statistically significant influence on students’ attitudes towards the use of Socrative applications for online teaching. Conclusion: According to the findings, Socrative application appears to be suitable for remote teaching and learning during the covid-19 pandemic in this digital era because of the most important characteristics of Socrative application, such as its ease of use, straightforwardness, minimal hardware requirements, and a long list of useful features.

Evaluation and Perception of the Immediate Impact of COVID-19 on Dental Education: An Observational Study.

Background The primary aim of the studyis to assess how the COVID-19 pandemic has affected dental education for students and teachers, particularly concerning the efficiency of online teaching strategies for both clinical and academic outcomes. Additionally, it also emphasizes psychological behavior like stress and anxiety in the post-pandemic era due to academic pressure. Materials and methods The study included 300 undergraduate dental students from various years of studying and 31 teachers actively teaching in dental college from all nine departments. The questionnaire was created in Google Forms for both students and teachers. The questionnaire contained various aspects of an education system that was adopted post-pandemic. The questions were given to evaluate the opinions of students as well as teachers to understand which method of teaching is preferable for them. Whether it's an online method of teaching or the offline mode, out of which the majority of respondents preferred the offline method of teaching. Result Most of the respondents agreed that the pandemic had affected their academic as well as clinical performance. Online mode of learning was not as effective as face-to-face learning. It did not affect the students clinically but mentally, which made them more anxious and stressed out during their studies and preparation for exams.Teachers too had to face a lot of problems from technical aspects while teaching onlinewhichled to mental stress and anxiety. This study re-emphasizes the negative impact of COVID-19 on dental education. It also further highlights the more fundamental changes and improvements that are desperately needed in the education system to adapt and overcome the current pandemic situation and future such events. Conclusion The COVID-19 pandemic has significantly impacted educational institutions, curricula, and clinical practice. Dental students are particularly vulnerable, and online education platforms in India face challenges in regulation. The quality of materials, technical assistance, and expertise in online classes significantly impact teaching and learning experiences.The pandemic has led to concerns about students and staff contracting the disease, creating a skill gap in dental graduates, and loss of clinical exposure, causing more anxiety. To address these concerns, regular, open, and compassionate communication with students is essential. Educational institutions must undergo a transformation to adapt to the pandemic and potential future disasters.

Research on quality evaluation of online teaching platform based on RS-BP Neural Network

Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms.

Developing Medical Students as Educators: A Course Applying Teaching and Learning Theory to Question Writing.

There is a need for formal students-as-teachers education with authentic application. We report on a course that teaches and models effective educational theory. Students applied theory by writing questions with answer explanations which were incorporated into an online educational platform used by first-year medical students.

Factors to determine the adoption of online teaching in Tanzania's Universities during the COVID-19 pandemic.

Face to face mode of delivery has been a standard method of teaching courses in the majority of African Universities Tanzania included. The COVID-19 pandemic has caused the closure of all schools and universities worldwide; therefore, face-to-face teaching is no longer the only appropriate and feasible teaching method. This requires changes in the teaching method with the remarkable rise of e-learning, whereby teaching must be undertaken remotely and on digital platforms. The main objective of this study is to determine the perceived factors that hinder the adoption of online teaching in Tanzanian universities. A mixed method dominated by a quantitative approach was used to answer the research questions. A total of 173 academic and ICT staff from nineteen universities in Tanzania participated in this study. Descriptive statistics (univariate analysis) and binary logistic regression were applied to analyse the frequency and compare the mean to describe the characteristics of respondents and determine the factors influencing the academic staff to have an online course. This was followed by content analysis to analyse the academic staff's proposed adaptation to online classes. The findings indicated that the main hindrances to online teaching are lack of capacity; knowledge on how to conduct online courses; knowledge and technological factors; environmental factors; staff attitudes towards delivering online courses, and years spent using eLearning. The academic staff proposed improving ICT infrastructure and strengthening the capacity of academic staff to provide online courses. The study concludes that most academic staff are willing to adopt online teaching while suggesting improving the organisational and individual factors to enhance online education. The paper recommends that the university use freely available online teaching tools and platforms while simultaneously focusing on addressing the corporate and individual factors identified in this paper to enhance online teaching, which is mandatory in response to lockdown measures.

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Abstract At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are becoming more and more prominent. This paper firstly analyzes the discrete regression model, and after comparing the logit model under random utility selection and its three derivative models, this paper selects the most widely used hybrid logit model to evaluate and analyze the online teaching platform of university public sports tennis with the MNL model of the maximum likelihood method for error analysis. Secondly, the overall architecture of the online teaching platform system is proposed, and the online teaching platform student satisfaction is studied and analyzed. Finally, based on the hybrid logit model, the AIC, BIC, and log-likelihood value indexes of the online teaching platform for college public education tennis are analyzed for the goodness of fit. The results of the study showed that students were more concerned about three aspects of the online teaching platform: operational difficulty, course comprehensiveness, and learning efficiency, which were 65%, 69%, and 74%, respectively. The AIC and BIC indicators of the hybrid logit model were 4753.656 and 4901.321, which were 192.142 and 117.848 lower than those of the conditional logit model, respectively, indicating that the model fits well and provides a better evaluation and analysis of the platform.

Retracted: Design of Personalized Ideological and Political Education Resource Recommendation System in the Online Education Platform in the Network Environment.

[This retracts the article DOI: 10.1155/2022/3845749.].

Effectiveness of electronic communication teaching mode in mitigating learning anxiety among university students

BackgroundLearning anxiety disorder detrimentally impacts both academic performance and the mental well-being of university students. This study aims to investigate whether the integration of electronic communication technology into teaching methods within universities can alleviate students’ learning anxiety and assess the efficacy of this approach.Subjects and MethodsParticipants were selected from a university and divided into an experimental group and a control group. The experimental group underwent classroom teaching using electronic communication technology, encompassing online teaching platforms, virtual laboratories, and other relevant tools. In contrast, the control group received traditional face-to-face teaching. Anxiety questionnaires and academic performance metrics were employed as data collection instruments. Data were analyzed using Stata 8.0 statistical software.ResultsThe learning anxiety scores of students in the experimental group were notably lower compared to those in the control group. Specifically, the average anxiety score for the experimental group was 3.82, while the control group had an average anxiety score of 4.96. Statistical analysis utilizing appropriate methods confirmed a statistically significant difference between the two groups (P<0.05).ConclusionsEmpirical evidence from this study substantiates that the integration of electronic communication technology effectively mitigates symptoms of learning anxiety among students and enhances their learning outcomes. Utilizing online teaching platforms, virtual laboratories, and similar tools empowers students to engage in more self-directed and proactive learning, consequently reducing anxiety that may arise from face-to-face teaching scenarios.AcknowledgementThe Philosophy and Social Science Research Project of Higher Education of Jiangsu Province (No. 2021SJA2140).

Investigation the Effect of Covıd-19 Pandemic in The Sales for Online Education Using Machine Learning Methods

Due to the pandemic which is the cause of the COVID-19 virus that emerged in 2019, many educational institutions had to follow online (remote) education. The situation in which the content of the pandemic occurred was also the reason for the education preferences of the users. The aim of this study is to analyze the effect of the pandemic, which includes the number of registered users of one of the online education platforms operating in Turkey thanks to machine learning, on distance education sales, and to create strategies by making sales forecasts for the future. Seven independent and one dependent variable were used to make sales forecasts using the data of the education structure. For accurate modelling, machine learning methods were first applied for decision analytics in a univariate manner and then multivariate applied and the applied methods were tested for error. By testing the success of the prediction models created with machine learning used in the study; 91.43% for support vector machine (SVM), 92.02% for multi-layer perceptron (MLP), and 96% for Long / Short Term Memory (LSTM). K-Folds cross validation method was also used for the success return of the established model.

Utilizing educational apps and online platforms for home economics learning: An investigation of students' attitudes and perceptions

This systematic review explores the utilization of educational apps and online platforms in Home Economics learning, focusing on investigating students' attitudes and perceptions towards these digital learning tools. As the world increasingly embraces technology in education, Home Economics, an essential subject that empowers students with practical life skills, benefits from these innovations. The review encompasses a comprehensive search for and analysis of relevant literature from various academic databases. Studies that examine educational apps and online platforms for Home Economics learning and assess students' attitudes and perceptions are included. Qualitative approach is considered to provide a comprehensive overview of the topic. Findings reveal valuable insights into students' preferences, motivation, and engagement with digital learning resources in Home Economics. The review explores factors influencing students' acceptance and resistance to adopt these technological tools in their learning journey. Furthermore, the analysis identifies potential challenges and benefits of integrating educational apps and online platforms in Home Economics education. Ultimately, this systematic review contributes to a deeper understanding of the role of educational apps and online platforms in Home Economics learning and offers recommendations to educators and policymakers for effective integration and enhancement of digital resources in the field.

Results From a Multi-Institutional Pilot Study of iContour, an Interactive Online Platform with Real-Time Feedback to Improve Contouring Education for Radiation Oncology Residents

Numerous studies have shown that variability in contouring by radiation oncologists is common and associated with poor clinical outcomes. Contouring is taught via an apprenticeship model during residency with inconsistent results. Currently, there is no standardized contouring curriculum. We hypothesized that an interactive online educational platform for learners to practice contouring and receive real-time visual feedback would be a useful curricular tool. The iContour platform displays anonymized DICOM data and allows for input and analysis of user contours in a web-based interface. Nine cases are available from the Head/Neck (H+N), Gynecologic (Gyn), and Gastrointestinal (GI) disease sites. The system presents users with a case and asks them to contour representative slices from 2-3 target volumes or OARs. Upon submission, users are shown several forms of feedback. These include immediate visual comparison with expert contours on the same dataset (all cases), customized feedback based on overlap with prespecified "avoidance" and "inclusion" structures that highlight common mistakes (Gyn cases), and tumor control probability models to estimate the clinical impact of a user's contour variations on patient outcomes (H+N cases). Some cases include short videos outlining anatomy and contouring principles which are shown before the user contours. A pilot study was performed to evaluate technical performance and educational utility. Pre- and post-surveys with Likert-type questions (1-5 scale from strongly disagree to strongly agree) were used to assess user satisfaction and preferences regarding feedback. A total of 9 residents participated (median PGY3, range PGY2-5) from 5 institutions in 2 countries. Each participant completed 2 cases from a single disease site (n = 3 each for H+N, Gyn and GI), one with educational videos and one without. 67% of users had completed a prior clinical rotation in their disease site. Overall, residents felt the system was a useful educational tool (mean Likert score 4.67 +/- 0.47) and were interested in using it during clinical rotations (4.89 +/- 0.31). Most participants (7/9) felt iContour was more useful than existing resources for contouring education. Residents unanimously (9/9) found direct visual comparison with expert contours the most useful type of feedback, and that cases with videos before contouring were more educational. The iContour platform is a useful educational tool for radiation oncology residents. Participants felt receiving immediate visual feedback on contours was a valuable learning experience. Short instructional videos before contouring can be utilized to provide "just in time" teaching. A randomized study to formally assess the platform's impact on contouring skills is planned.

Cybersecurity, Safety, & Privacy Concerns of Student Support Structure for Information and Communication Technologies in Online Education

COVID-19 has created a dramatic paradigm shift in education methods, which forced schools and universities to abandon the usual in-person education in favor of online education modules. Such a shift has extended the time and use of internet communication technologies (ICTs) by most, making online education platforms primary cyberattack targets. In this context, this study aims to explore parents, educators, and other caregivers' concerns about online education and the cybersecurity of their children and students. Thus, we conducted a survey-based study with 983 participants recruited through popular crowdsourcing platforms: MTurk and Prolific. Our results indicate a lack of technical support following cyber safety that the students received with the sudden transition to online education. Over 31% of our participants claimed that they never or rarely receive any communication related to cyber safety from the students' educational institutions. Additionally, our analysis shows that the student support structure needs to be trained and informed on the threats faced by children online and on the ways to mitigate these threats. Finally, we find a statistically significant difference between parents, educators, and other caregivers regarding their perceptions of children's online privacy and cyber safety. We conclude this work by providing actionable recommendations to promote privacy-preserving and digitally secure online education.

Learning about fostering student engagement during online teaching: A collaborative reflective inquiry of two teacher educators

The rapid shift to online teaching and learning during the COVID-19 pandemic required lecturers to think of altering their pedagogical approaches to suit online learning. One of those pedagogical approaches is fostering student engagement. We are two teacher educators who collaborated using a self-study methodology to look into our practice to learn about fostering student engagement in an online teaching environment. This study was guided by the theory of experiential learning and collaboration. Data were collected from our journal entries and collaboration discussions to narrate how our collaborative endeavours enabled us to learn about fostering student engagement in an online teaching platform. While the findings of what we learnt about fostering student engagement when teaching online are not entirely new in literature, we found that our collaborative endeavours helped us to deal with our fears and vulnerabilities as well as noticing pertinent aspects of student engagement in online teaching. We recommend that lecturers need to embrace collaboration in negotiating new and challenging educational situations.

Leadership in Online Education: A Scoping Review

Over the last two decades, we have witnessed a growing interest in online learning. There is an increased focus on online education research due to the COVID-19 pandemic, as the crisis led to online education platforms becoming a necessity for educationists and students. This article reviews online education, purely, in the context of leadership. Past reviews have focused on concepts surrounding online learning such as technology, pedagogy, and student satisfaction. However, limited attention has been drawn to leadership exclusively in relevance to online education. The scoping review analyses 63 articles published between 2002-2022 to answer two research questions. First, what are the research concepts, theories, models, and frameworks operationalized in the publications within this topic domain? Second, who are the stakeholders and leaders identified in literature? Through synthesizing alternative research perspectives in the field of leadership, education, and information and communication technology (ICT), this review identifies four categories of research literature relevant to the topic domain. Following themes emerged from the analysis of the publications: impact of leadership on online education; student experience in an online leadership programme; impact of online education on leadership development of actors in an online education setting; and perception of leadership in an online education setting. Based on the review, an agenda is proposed for future research to enhance our understanding of the role of leadership in online education and elucidate the relationship between leaders and online learning in educational institutions. A lack of focus on the key leaders in the existing literature is made apparent, and further empirical research is recommended. This review makes a theoretical contribution by consolidating existing research in leadership in online education through the synthesis categories and identifying future research pathways. The findings have practical implications for leadership in online education in educational organizations through identification of stakeholders and the entities in leadership positions.

The Effect of Strengths/Opportunities and Weaknesses/Challenges on Online Learning during the COVID -19 Pandemic

This paper researched the effect of strengths, opportunities and weaknesses or challenges on the online learning process during the COVID-19 pandemic. The research approach focuses on curriculum implementation and teaching quality using information technology systems (online teaching platforms). The research included 146 teachers (lower secondary and upper secondary school level), from whose responses we identified the strengths and opportunities as well as the weaknesses and challenges of online learning. While analyzing the data, we tested the construct reliability and noticed that Cronbach’s Alpha turned out to be at an acceptable level. From the paired sample t-test data analysis, it has also been concluded that, in the online learning process, strengths and opportunities are more considerable than weaknesses and challenges. Furthermore, linear regression indicated that the adjusted R-square of this model is 0.353, which signifies that the independent variables, strengths and opportunities and weaknesses or challenges, have an effect of 35.30% on the dependent variables, the curriculum implementation and the teaching quality, when using the information technology systems. As a result, we reached the conclusion that the positive effect of strengths and opportunities is higher than the negative effect of weaknesses or challenges.

Research on the Construction of Educational Practice Curriculum Integrating Ideology and Politics Under the New Situation

Online education internship is an emerging education and teaching method that is increasingly attracting people's attention in China. It plays a very important role. The main object of online education practice courses is college students, and its teaching content includes three aspects: science and technology, social science and humanities. It records and propagandizes through video and can also be provided to the public for free, With the continuous development of computer technology, a new online open teaching mode has emerged. The increasing popularity of using computer networks to carry out online educational practice courses not only demonstrates that modern education is gradually developing towards networking, multimedia, and video, but also poses great challenges to the current higher education itself and provides us with great inspiration, especially for the reform and transformation of ideological and political education courses in universities. The goal and significance of this study is to establish an online education platform for college students, enabling them to leverage its advantages.

Adaptive learning-based content management tool for online education platforms

The ways of accessing information are highly developed today. Online education platforms are widely used directly or indirectly in the education of students. Instructors create their course content on these platforms and teach their courses to their students. With the developing internet technologies, the variety of visual, audio and textual course content is also increasing. However, students' learning tendencies differ while studying these course contents. While some of the students can learn more easily from course content consisting only of texts, others can learn more easily from course content supported by audio-visual materials. Identifying these learning differences among students has become important today. In order to enrich learning activities, it would be useful to create content in accordance with the learning tendencies of each student. In this study, we developed an adaptive learning-based help tool for instructors to create course content. This tool analyzes the learning styles of the students and provides recommendations to the instructor for creating the course content. Thus, all the course content prepared by the instructor will be selected and created according to the learning tendencies of the students. It will contribute to the improvement of learning activities.

Application of Big data connection processing technology in the construction of an online aesthetic education platform

Abstract Exploring the application of big data connection processing technology in constructing online aesthetic education platforms to improve the operability of online aesthetic education. This paper uses big data technology to construct an online aesthetic education platform based on MapReduce distributed parallel computing framework. The Bloom Filter algorithm is used to analyze and process the online aesthetic education data, and the Bloom Filter algorithm is improved with the hash function. The filtering optimization strategy based on MapReduce distributed parallel computing framework is used to analyze further and process the online aesthetic education data, and the error rate and execution efficiency of the online aesthetic education platform is analyzed using the test data set. Regarding the misjudgment rate, when the number of hash functions is 4, the misjudgment rate of the platform reaches 1.24% at the minimum. The execution efficiency of the online education platform is 102s when the number of Reduce is 10-20, which indicates that the use of big data connection processing technology can effectively realize the construction of online education platforms and provide a new research direction for the realization of information-based education.

Online Versus Traditional Classes: The Case of History Teaching

Higher education has undergone a massive change due to the COVID-19 pandemic worldwide. Many higher education institutions were unprepared for the disruptive effects of the pandemic but the adoption of online teaching and learning platforms was fast and successful for most of them. This sudden change brought new challenges and constraints as well as novel opportunities and perspectives into higher education in general and teacher training programs in particular. In order to provide deep insights into prospective teachers’ experiences with online learning mode, this longitudinal case study aimed to compare the initial perceptions of the participants of online education with those elicited nineteen months after the start of the pandemic. Given that the initial views can be misleading, it is assumed that a comparison between the initial and latter views of online learning would be well-founded, which would have sound implications for the future of online teaching and learning platforms for the post-COVID times. Nine undergraduate students studying in the 96-436x department of a private university in Nicosia participated in the current study. The data were elicited via reflective essays to be analyzed qualitatively. Overall, the results indicated that online mode of instruction and learning was not favored in the beginning of the pandemic since all the themes emerged were negative. However, the analysis of the latter data indicated that the participants’ views changed for the better dramatically. Hence, the results have a few implications for the future of higher education

Online educational video engagement prediction based on dynamic graph neural networks

PurposeWith the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.Design/methodology/approachThe quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.FindingsModels with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.Research limitations/implicationsA limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.Originality/valueIn this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.