Creating Virtual Community For Older Adults: During the COVID-19 Pandemic And Beyond

Combining data on health and well-being from the University of Michigan National Poll on Healthy Aging (NPHA) with case studies and data from GetSetUp, a virtual online learning community, and the Michigan Department of Health and Human Services (MDHHS), this symposium will highlight how virtual community can be created and supported during the COVID-19 pandemic and beyond. Polling data on loneliness and physical environments demonstrate the need for opportunities for connection before and during the pandemic. Other polling data from the NPHA shows telehealth visits increased significantly as did the use of video chat technology. These findings suggest that comfort with technology may help support aging in place. GetSetUp helps to make this possible with customized learning to help older adults overcome hurdles to tech adoption and use. GetSetUp classes focus on supporting social connection and providing information on resources and services. Beyond the pandemic, these services will remain critical for many older adults, including those facing mobility limitations, those with limited community, and those looking to diversify their networks. The Senior Deputy Director of Aging and Adult Services Agency will highlight how Michigan combines data and technology to support Michigan’s aging network. The GetSetUp and MDHHS virtual community allowed for a statewide connection to health and aging services, including programs such as vaccine information sessions. The data and case studies described will highlight the need for connection during the COVID-19 pandemic and how a startup and State worked together to address this need.

An Evaluation of the Flipped-Learning Pedagogical Approach

In recent years the emerging possibilities in the education system are flexibility (Nuhoğlu et al., 2020) and self-paced learning (Priscila, 2020). The flexibility of studying anywhere and anytime can provide opportunities for learners to achieve their educational goals. A self-paced and customized learning environment could enhance the learning experience of the students. This research evaluates flipped-learning pedagogical approaches for year 13 Maths students. Quantitative and qualitative data collection methods track and monitor students’ academic outcomes. The findings suggest that flipped-learning improved students’ academic achievement and progress. Additionally, students who missed the lessons could understand the concept and complete learning activities before coming to the next lesson. A flipped-learning approach has encouraged the students to make them responsible for their learning, bringing questions to the classroom to extend their conceptual understanding, and develop mathematical reasoning and thinking skills.

Customized Learning Model Using Learner Activity Analysis

Reshaping education with knowledge and skills is the major objective of many of the developing organization to build competitive knowledge economy. Electronic learning, also popularly known as e-learning is one of the most effective applications of Information and Communication Technology (ICT), which refurbishes the dimension in higher education. COVID-19 pandemic situation justifies the need and real utilization of E-learning. Existing E-learning system provides static content to all learners irrespective of their learning needs. In this paper we propose to create Dynamic Role based access control model based on information from learners needs, audit logs and observations to support adaptive E-learning systems.

Pelatihan Literasi Digital Pada Sekolahrumah Salihah Yogyakarta: Animasi, Poster Digital, Video Dan Gim

Young people could learn and use technology in formal, informal and nonformal education effortlessly. As a legal nonformal education, homeschooling programs become more popular because the programs demonstrate some advantages, such as customized learning materials, personalized learning methods, and flexible learning schedules. However, some homeschooling communities face several problems related to digital literacy skill because of lack of teachers’ capacity and tools. To support digital literacy, a series of training has been conducted for teachers and students at Salihah Homeschool, Yogyakarta. It consisted of training of modern computer technology for teachers and multimedia training for students and parents. The second training taught students on how to make digital posters, videos, animations, and games related to Covid-19 pandemic. Survey showed half of the students were happy with the training activities. Furthermore, the most preferred lessons were animation and digital posters, while game and video tutorials were perceived difficult. Nevertheless, student participation showed a declining trend since the second day of training. Moreover, some parents expressed happiness with the training contents, but there were also parents who found difficulty as the parents were novice users. It implies future efforts to promote positive awareness of the ease of using IT and continuous monitoring to improve digital literacy.

Adaptive E-Learning System Using Justification Based Truth Maintenance System

In most E-learning systems, educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E-learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper, we suggest an Adaptive E-learning system which is providing adaptability with support of justification-based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the student’s profile, interests, and previous results.

AI-Based Virtual Teacher Assistants

Современное образование находится на рубеже цифровой эпохи в преподавании, когда повсеместно внедряются новые методы обучения с использованием передовых информационно-коммуникационных технологий для дистанционных форм общения между педагогом и слушателями, автоматизации образовательного процесса с применением нейронных сетей и машинного обучения. Разрабатываемые цифровые образовательные среды и платформы не только позволяют учителю готовить и предоставлять школьникам и студентам учебный материал в удобной цифровой форме, которые доступны им в любом месте земного шара через сеть Интернет, но и существенно увеличивают нагрузку на преподавателя ростом объема необходимого для подготовки занятий образовательного контента. При этом преподаватель на дистанционной форме обучения фактически вынужден находиться круглые сутки на связи со своими слушателями. Использование цифровых образовательных сред с автоматизированной проверкой заданий позволяет освободить педагога от части рутинной работы. Другая возможность состоит в использовании в курсах виртуальных ассистентов преподавателя чат-ботов, обученных с помощью нейросетевых технологий отвечать на широкий круг вопросов студентов, без участия педагога. В статье излагаются результаты исследования и разработки подобных виртуальных помощников педагога. Одновременно указывается, что собранный материал диалогов бота со студентами позволяет использовать результаты для косвенного анализа успеваемости студента и формирования персональных образовательных траекторий. Today the education is on the verge of the digital age as new learning approaches with advanced information and communication distant learning technologies, and academic process digitalization with neural networks and machine learning. The new digital learning environments and platforms help the teacher to develop and deliver digital learning content for high school and university students making it available globally over the Internet. However, the teacher efforts are multiplied, as more and more content is to be developed. Even more, an elearning teacher is expected to be available 24/7. Digital learning environments with a computerassisted assessment feature remove some of the burdens from the educator. Another option is using virtual teacher assistants or chatbots based on trained neural networks. they can answer a wide range of student questions without the teacher’s intervention. We studied the development of such virtual teacher assistants. The recorded student/chatbot scripts can be used to indirectly estimate the student’s academic performance, and to offer a customized learning path.

A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.