SI-BIME Smart Learning Multimedia Platform for Students: a Solution for the Pandemic-19 in the Regions

The impact of the Covid-19 pandemic is not only experienced by a handful of people but by all lines of life in this world. One of the biggest impacts is related to the provision of education at the primary, secondary, and senior levels. This study uses the prototype method which is targeted to produce applications that are ready to be implemented to produce an intelligent learning application. This study uses a quantitative method for evaluating results. This study aims to assist schools, especially those in the regions, in overcoming the problem of decreased learning activity because it has been identified as the cause of decreased student learning outcomes. This research was conducted at the high school level because students were prepared to continue to a higher level. This research resulted in a smart learning SI-BIME (BINUS Multimedia Edutainment Information System) which was prepared to support e-learning-based learning processes in schools. Based on the evaluation of learning, the results of this study proved to be very helpful for schools and support a better learning process. As well as focus group discussions with school authorities, students, and parent representatives, assessments are also provided by higher-level authorities. The results of this study can also help schools and teachers to prepare better learning media for students and can be used comprehensively and sustainably even though COVID-19 is over. © 2022 IEEE.

Design of Online-Merge-Offline Intelligent Learning Space under the Background of Open Education

The global COVID-19 is spreading, and online teaching is developing rapidly. The continuous deepening of the integration of new technologies such as artificial intelligence and big data with education and teaching has prompted new changes in education and teaching, especially online and offline integrated teaching has become a new form of teaching. Combining the characteristics of open education, this study proposed a design model of online-merge-offline (OMO) intelligent learning space under the framework of PSST on the basis of sorting out the connotation of OMO intelligent learning space, in order to provide reference for future research on intelligent learning space. © 2022 IEEE.

Smart Learning Environments: Overview of Effective Tools, Methods, and Models

Digital learning environments have undergone a zigzagging evolution over the contemporary history of intelligent learning environments. In the pre-COVID-19 phase, e-learning struggled to establish itself in traditional training systems, but since the pandemic outbreak of March 2020, distance learning has become the only possible way to use the training actions. Today’s debate following this enormous experimentation has produced tools, methods, and models that need a further rethink for the post-COVID-19 phase. A possible evolution of full online education is a hybrid version of learning environments in which online and in-person, tangible and digital, alternate in time, space/place, media technology, learning design, and content coexist. These five categories guide the structuring of intelligent environments and adapt to the needs of students, teachers, and the social context in which they are inserted. Although the design follows recursive patterns, it is extremely flexible and adaptable. Furthermore, these digital environments make it possible to convey specific self-regulated learning methods and to develop specific motivational methods aimed at self-determination. The models of hybrid learning environments differ in the purposes to be pursued or the type of users to be reached. The surveys and experiences gained in the sector of innovative teaching methodologies find their most important field of application in hybrid environments. The purpose of this chapter is to summarize the future applications of the results that emerged from the experiments conducted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluating Performance on Covid-19 Tweet Sentiment Analysis Outbreak Using Support Vector Machine

Sentiment analysis is a perfect machine learning process to analyze text and returns the text whether in positive or negative. The machine is trained with the emotions in text, then the machine can automatically understand text and predict the sentiment analysis. Sentiment analysis is an information extraction task that gives the result based on users writing emotions such as positive and negative thoughts, feelings. The emotions can be categorized as positive or negative words. Now, natural language processing (NLP) is an upcoming field in machine learning which gives hybrid applications in daily life. For example, the keyword which is taken from the text will undergo for intelligent learning. The output of the NLP algorithm enables sentiment analysis report daily activities. In this paper, we exposed the Covid-19 tweets from social media and did sentiment analysis using support vector machine (SVM). We trained the system using sentiment model and found the emotions from the Covid-19 tweets. Based on the trained system, we found the emotions in terms of negative, positive, and neutral emotions from Covid-19 tweet messages. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Intelligent Digital Learning Platform to Enhance Digital Health Literacy

Nowadays spreading of COVID-19, people are more at home and less physically active in the long run, which may affect their health. Moreover, currently, there are many facilities that make human life more comfortable. Various technologies are being used to assist with daily activities. Including the perception of news has changed to the perception of news through the Internet. For this reason, this research has been working about intelligent digital learning platform to enhance digital health literacy, the research objectives are as follows. )1 synthesize documents and international research of intelligent digital learning platform to enhance digital health literacy )2design an intelligent digital learning platform to enhance digital health literacy )3 assess the suitability of intelligent digital learning platform to enhance digital health literacy.Will use ten experts as assessors. Those ten peoples are divided into five computer engineering technology experts and five medical technology experts who have more than five years of related experience. The research instruments were a suitability assessment form for intelligent digital learning platform to enhance digital health literacy.The results of this research found that the intelligent digital learning platform to enhance digital health literacy, was developed and it was appropriate © 2022, International Journal of Emerging Technologies in Learning. All Rights Reserved.

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework.

Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.