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In the context of the development of the digital economy, it is necessary to introduce elements of distance education technologies into the educational process. This will make the learning process continuous. Distance technologies allow you to master programs of higher and secondary education (especially in correspondence and full-time forms) on the job. The introduction of distance learning technologies in the educational process increases students ‘ interest in the subject being studied. In modern conditions, the development of online courses is relevant. To increase the interest of students in online courses, it is recommended to involve representatives of employers to record lectures on specific topics within the online course. An important role is played by the introduction of elements of remote technologies in the organization of current and intermediate certification.The article is written on the basis of the author’s own pedagogical experience. Systematizes the directions of introduction of distance educational technologies that can be used both for the organization of the educational process and for all types of control of students knowledge. The article dis-cusses the advantages and disadvantages of distance learning, as well as the possibility of using elements of distance learning technologies in the implementation based correspondence courses that will be relevant in the implementation of federal state educational standard 3++ (including professional standards).Examples of experience in organizing the educational process with elements of distance learning are given.
One of the most important pillars of smart cities is the smart learning environ-ment. This environment should be well prepared and managed to improve the in-struction process for instructors from one side and the learning process for stu-dents from the other side. This paper presents the student’s Engagement, Behav-ior and Personality (EBP) predictive model. This model uses Moodle log data to investigate the influence and the effect of the students’ EBP factors on their per-formance. For this purpose, this paper uses the data log files of the "Search Strat-egies on the Internet" online course in Fall 2019 at Sultan Qaboos University (SQU) extracted from Moodle database. The intention of conducting this kind of experiments is of three-facets: 1. to assist in gaining a holistic understanding of online learning environments by focusing on student EBP and performance with-in the course activities, 2. to explore whether the student’s EBP can be considered as indicators for predicting student’s performance in online courses, and 3. to support instructors with insights to develop better learning strategies and tailor instructions for personal learning of individual students. Moreover, this paper takes a step forward in identifying effective methods to measure student’s EBP during the learning process. This may contribute to proposing a framework for the smart learning behavior environment that would guide the instructors to ob-serve students’ performance in a more creative way. All the 38 students who participated in this experiment had compatible statistics and results as the relationship between their Engagement, Behavior, Personality was symmetric with their Performance. This relationship was presented using a group of condition rules (If-then). The extracted rules gave us a straightforward and visual picture of the rela-tionship between the factors mentioned in this paper.
Education is a process that simplifies learning. It should be a continuous process in one’s life to attain success. Over the decades, Information and Communication Technologies (ICT) have gradually begun to play a very important role in education, with their usage in education is growing worldwide continuously. These methods allow access to information through the internet. In education, ICT is the method of teaching that uses information and communication technology to support, improve and optimize the transfer of information, in turn leading to the invention of smart education. This improves the quality of teaching, the learning process of students and ultimately facilitates e-learning. It is commonly believed that technology can empower educators and students, making substantial contributions to knowledge and achievement. This paper gives an insight into the various tools that help instructors to develop online course content using Learning Management System tools. These tools allow the instructors to conduct online classes from any location using tutor tools and desktop recording tools to record screen output for further use. The instructor can assess the students in their course using assessment tools and can also enhance teaching methods using innovative teaching tools. The paper also throws limelight on the feedback taken by the faculty as well as the students about the usage of various tools in higher education which helps in analyzing the best suitable tools.
The implementation of distance learning is carried out with the help of modern systems of distance education. They allow to teach and to assess the knowledge of interns and doctors quickly and easily, regardless of their location. The aim of the study. A comparative review of the most well-known distance learning platforms, wich are designed to organize the learning process and control learning with the help of Internet technology. System of distance education is a virtual classroom with the possibility to train interns and doctors from different regions of Ukraine at the same time. There are many educational platforms for distance learning nowadays, such as Moodle (Australia), iSpring Learn LMS (Russia), Collaborator (Ukraine), eTutorium LMS (Ukraine), Opigno (Belgium), Atutor (Canada). Moodle is a free platform that allows users to create individual courses. It supports more than 100 languages. iSpring Learn LMS is a simple and user-friendly system that is a paid alternative to Moodle. Collaborator is a platform that works effectively on all modern devices and browsers and is virtually independent of the software of the user's device. eTutorium LMS is a virtual distance learning system that allows to create an online course of any complexity quickly. Opigno is a modern free distance education system based on Drupal (a popular content management system). Atutor, like Moodle, is an open web-based e-learning system. Conclusion. Distance learning systems differ not only functionally, but also in the way they solve problems. The simplicity of use of the platform depends on the degree of its adaptation to the needs of the user and the ability to use all existing features and functions of the system.
Technology and Inquiry-Based Instructional Methods: A Design Case in Student-Centered Online Course Design
Although online course design is no longer new, few design cases describe the development of entire courses based on principles of student-centered learning design. This design case chronicles the context, design challenges, and successes and failures of a graduate course on Technology & Inquiry-based Instructional Methods for an online master’s program in educational technology at a regional university in the southwestern United States.
PurposeThis paper explores community college entrepreneurship education's near-instantaneous transition to online course delivery following the 2020 COVID-19 pandemic.Design/methodology/approachPrimary data were obtained from 92 community college entrepreneurship faculty via online survey in late March of 2020, right at the time faculty were required to transition their courses to an online mode of delivery due to the COVID-19 pandemic. Data were collected in partnership with the National Association for Community College Entrepreneurship and the Entrepreneurship Education Project.FindingsWhile the majority of community college entrepreneurship educators have taught online previously, many were not familiar with exemplar education technology tools and applications, demonstrating an opportunity for continued professional development. To deliver courses online, educators primarily relied on pre-recorded lectures and using Zoom as the technology platform of choice. Last, there were significant faculty concerns about their ability to effectively create an “experiential” classroom virtually for students to learn and practice entrepreneurship.Originality/valueThis is the first paper investigating how community college entrepreneurship educators responded to one of the most disruptive events to ever impact entrepreneurship education (viz. the COVID-19 pandemic). More broadly, this is also one of very few studies exploring both (1) community college entrepreneurship education and (2) how unexpected crises (e.g. natural disasters, pandemics) impact educational environments.
Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses
In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.
Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation
The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.