Extracts of Children’s Books as a Basis for Creative Writing Activities Within the Framework of Distance Learning:An Elementary School Case Study

Literature can be used in education as basis for creative writing activities not only in traditional classroom learning but also in distance learning.The purpose of this study was to evaluate the effects of a four-month online creative writing program on the utilization of literary texts in elementary school through collaborative activities.The research sample consisted of 15 students of the 5th grade of elementary school.Evaluation of program activities was performed by completing a structured improvised questionnaire on the asynchronous education platform“e-class”of the Panhellenic School Network,where students were asked to fill in fields regarding(a)literary excerpts,(b)creative writing activities,and(c)online groups.Based on the results,students responded in a positive manner to the use of literature in the context of online collaborative writing groups.The majority of students expressed their satisfaction both with regard to the creative writing activities,having literary excerpts as a stimulus,but also with regard to the effectiveness of online groups.In conclusion,it seems that literature can be used creatively through playful group creative writing activities and distance learning.

Development and Evaluation of a Distance Learning System Based on CSCW

This paper described a distance learning system, which allows Internet users to conduct a lesson in real time from any kinds attached computers. Participants can jointly view and edit relevant multimedia information distributed through Internet. Teachers and students can also simultaneously communicate by voice and text to discuss the problems. Teacher can broadcast streaming PowerPoint presentation in real time to network users. In addition to sliders, presenters can broadcast video and audio simultaneously to deliver a live multimedia show online, and store their presentations for on demand playback. Teachers distributed in different places can also use cooperative editing tool to edit and encode existing digital content. We discussed some important design principles of the system. Then, the system configuration and the results of evaluation are also presented. The system has proved to be applicable to the distance learning based on CSCW (Computer Support Cooperative Work) in Internet.

An Exploratory Study of the Application of Modern Distance Learning Resources for Rural Elementary and Middle Schools in Western China:The Case of Three Cities in Gansu Province

Based on a specially adapted questionnaire,research was carried out to explore how resources in modern distance learning were being applied in rural elementary and middle schools in Western China,in order to enhance the usefulness of the allocated resources.One particular distance learning project was used as the subject of the study,and teachers at 22 rural elementary and middle schools in 3 prefecture-level cities in Gansu Province,Western China,acted as the sample group.Analysis of the teachers’questionnaire responses showed that,(1)there is a comparatively high number of modern distance learning resources in use in rural elementary and middle schools in Western China,and they are being effectively used in the classroom to assist in everyday teaching;(2)teachers of rural elementary and middle schools in Western China have the ability to make use of these modern resources,and can obtain them by way of multi-channel inquiry;(3)modern distance learning resources are already becoming important components of everyday teaching resources in Western China;and(4)teachers are generally able to apply these resources well,despite some problems remaining.In the case of problems in application,most teachers are able to find adequate solutions.

澜湄开放教育及柬埔寨远程教育的未来

This journal article delves into the future of distance learning in Cambodia and the potential for collaboration with the Mekong-Lancang Open Education initiative. It explores how distance learning can successfully address the current issues the Cambodian educational system is currently facing. The article discusses the goals and opportunities for collaboration that the Mekong-Lancang Open Education initiative presents, as well as the difficulties and potential solutions involved in implementing distance learning in Cambodia. Moreover,the article also offers insightful case studies and best practices from other countries, offering valuable insights and lessons for Cambodia. Lastly, the article concludes with policy recommendations for the future of distance learning in Cambodia. Future research and studies should concentrate on continually evaluating and improving the Mekong-Lancang Open Education Initiative to ensure that it effectively meets the educational needs of students and educators.

WhatsApp as a Tool for Integration and Motivation in Distance Education

This article was based on a Distance Education project in the Pedagogy course which, for a semester, through active methodology, reviewed the contents of the course to increase the knowledge of these students as well as to carry out a pilot test of Hybrid Teaching. The research aimed to contribute to studies on the use of WhatsApp as a tool that favors the integration and motivation of students in Online and Hybrid Teaching. The project included classes before the classroom lessons, physical and online presence of tutors and teachers, communication via WhatsApp, e-mail, Facebook and the online course platform to review and raise the course fee, raising doubts and encouraging the participation and integration of each student. The object of the study was the use of WhatsApp throughout the course, due to the adhesion of students and teachers using it as a tool for integration and motivation for the graduates of the 17 poles participating in the project. It is a descriptive research with a quantitative approach and characterized by a survey. 104 students participated in the survey, which guarantees a confidence level of 93%. A questionnaire was elaborated and requested, via Google Forms, to understand aspects of integration and motivation, containing 10 questions using the five-level Likert scale. It was possible to verify that the tool presented a 77% satisfaction index with the use of WhatsApp considering its positive influence in the best use of the studies while 90% considered WhatsApp important in the integration of students with students, and teachers with students. Also corroborated that the student interacts better with the studies when there is an interpersonal relation present, in this case, through the classes of study and face-to-face held weekly and the immediate communication provided by WhatsApp group chats. Finally, there was a strong correlation (r = 0.8) between the intensity of satisfaction with the use of WhatsApp in this project and the satisfaction with the continuity of the use of WhatsApp in other courses.

COVID-19 and information and communication technology in radiation oncology:A new paradigm

Due to coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus 2,there has been a major reallocation of resources that has impacted the treatment of many diseases,including cancer.The growing use of information and communication technologies(ICT),together with a new approach to work aimed at ensuring the safety of health care professionals and patients alike,has allowed us to maintain the quality of care while ensuring biosecurity.The application of ICT to health care(eHealth)aims to significantly improve the quality,access to,and effectiveness of medical care.In fact,the expanded use of ICT has been recognized as a key,cost-effective priority for health care by the World Health Organisation.The medical speciality of radiation oncology is closely linked to technology and as a consequence of coronavirus disease 2019,ICT has been widely employed by radiation oncologists worldwide,providing new opportunities for interaction among professionals,including telemedicine and e-learning,while also minimizing treatment interruptions.Future research should concentrate on this emerging paradigm,which offers new opportunities,including faster and more diverse exchange of scientific knowledge,organizational improvements,and more efficient workflows.Moreover,these efficiencies will allow professionals to dedicate more time to patient care,with a better work-life balance.In the present editorial,we discuss the opportunities provided by these digital tools,as well as barriers to theirimplementation,and a vision of the future.

An up -to -date comparative analysis of the KNN classifier distance metrics for text categorization

Text categorization(TC)is one of the widely studied branches of text mining and has many applications in different domains.It tries to automatically assign a text document to one of the predefined categories often by using machine learning(ML)techniques.Choosing the best classifier in this task is the most important step in which k-Nearest Neighbor(KNN)is widely employed as a classifier as well as several other well-known ones such as Support Vector Machine,Multinomial Naive Bayes,Logistic Regression,and so on.The KNN has been extensively used for TC tasks and is one of the oldest and simplest methods for pattern classification.Its performance crucially relies on the distance metric used to identify nearest neighbors such that the most frequently observed label among these neighbors is used to classify an unseen test instance.Hence,in this paper,a comparative analysis of the KNN classifier is performed on a subset(i.e.,R8)of the Reuters-21578 benchmark dataset for TC.Experimental results are obtained by using different distance metrics as well as recently proposed distance learning metrics under different cases where the feature model and term weighting scheme are different.Our comparative evaluation of the results shows that Bray-Curtis and Linear Discriminant Analysis(LDA)are often superior to the other metrics and work well with raw term frequency weights.

Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

Distance metric learning guided adaptive subspace semi-supervised clustering

Most existing semi-supervised clustering algorithms are not designed for handling high- dimensional data. On the other hand, semi-supervised dimensionality reduction methods may not necessarily improve the clustering performance, due to the fact that the inherent relationship between subspace selection and clustering is ignored. In order to mitigate the above problems, we present a semi-supervised clustering algo- rithm using adaptive distance metric learning （SCADM） which performs semi-supervised clustering and distance metric learning simultaneously. SCADM applies the clustering results to learn a distance metric and then projects the data onto a low-dimensional space where the separability of the data is maximized. Experimental results on real-world data sets show that the proposed method can effectively deal with high-dimensional data and provides an appealing clustering performance.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

Learning from the crowd:Road infrastructure monitoring system

The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular intervals to identify damaged road segments and road hazards. Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier. To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.

The Virtual Lab System

A virtual lab system is the simulation of real devices and experiments usingcomputer and network technology. It can make users do experiments easily, observe experimentphenomena and results through the remote terminal. Consequently, users can get final results toverify relative theory. The article analyses the features of virtual lab systems. A real virtual labsystem named 'Multimedia Virtual Lab for Digital Circuit Logic Design (MVLDCLD)' which has beendeveloped by the authors and their group is also presented.

Modelling the Dropout Patterns of MOOC Learners

We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.