ABSTRACT
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. IEEE
ABSTRACT
Internet of Medical Things is one of the fastest growing fields in technology, and it is predicted to bring about the largest technological delivery ever. Medical systems have been pushed to improve online services across the board by the COVID-19 pandemic, as the edge-enabled Online learning system in the healthcare system offers incredible opportunities. With the introduction of cutting-edge digital health technologies, patients will be able to avoid unneeded, preventative hospitalizations. Furthermore, the utilization of adaptable and readily available media by both patients and medical professionals would allow telehealth-based medical platforms to make healthcare more efficient and economical. © 2022 IEEE.
ABSTRACT
There is a great interest in online learning systems, especially due to COVID-19 pandemic. However, there are a lot of limitations and challenges of online laboratory learning systems. This paper presents an efficient technique that provides an intelligent virtual tutor for online laboratory environment, as in engineering and science sectors. Based on the analysis of the student's mouse activities, the AI virtual Assistant or virtual tutor will automatically estimate the difficulties that the student stuck during conducting the steps of lab's experiment. Hence, the virtual tutor can assist the student, accordingly. The technique is based on multi-threshold that are used to discriminate different levels of difficulties. The values of these thresholds are estimated and optimized via the genetic algorithm. The experimental results show that discrimination between different student behaviors can be achieved accurately and efficiently. © 2022 IEEE.
ABSTRACT
The online learning system during the Covid-19 pandemic is the best choice in the learning process, however, the quality of learning and knowledge transfer remains the top priority. An android-based blended learning system is used at the Faculty of Sharia and Law which presents the features needed in the online teaching and learning process by combining face-to-face learning using an e-learning system and a two-way communication system, so that maximum quality of learning can be achieved. © 2022 IEEE.
ABSTRACT
As COVID-19 has brought a major disruption in educational system, online learning has replaced the traditional classroom learning during the pandemic in many parts of the world. One of the inferior point of online learning is that it has less interaction between the instructors and the learners. Therefore, closed and active monitoring of the student's academic activities is especially required in this mode of learning. The predictive model becomes one of the main tool for active monitoring that allows the instructors to forecast the final performance of the students to determine appropriate guidance or attention for each student. In this study, predictive models based on machine learning models for student performance are proposed using the log files that record student activities in online learning system. Different sets of features are used to determine the most suitable machine learning model. Several preprocessing methods are employed to improve the performance prediction including handling imbalanced data with Synthetic Minority Over-sampling Technique (SMOTE) and choosing relevant features by XGBoost model. The model with the highest performance yields the prediction accuracy as high as 83.95%. © 2022 IEEE.
ABSTRACT
The government's policy to carry out physical distancing during the COVID-19 pandemic has changed the process of dance learning activities that are usually carried out face-to-face into an online learning system. This study discusses how technology can be used in the implementation of the educational dance method for prospective elementary school teachers. This study uses a qualitative approach, using observation, document studies, interviews, and field notes as data collection. The data analysis technique was carried out using descriptive analysis techniques. The descriptive method is used to describe the process and results of the implementation of online dance education. The results of the study indicate that technology can assist the practice of teaching dance education for prospective elementary school teachers, but it is not enough to help them in making dance accompaniment music and visualize group dance compositions. Based on the results of this study, skills in using technology will affect the success of online dance education learning. Based on these findings, prospective elementary school teachers need to be equipped with special competencies in applying technology that can be used to create dance music. © 2021 ACM.
ABSTRACT
The goal of this study is to find the best pedagogical idea for learning during the Covid-19 pandemic. The writing method in this article is the Systematic Literature Review method which aims to describe, study, and analyze students' pandemic pedagogy due to the impact of the Covid-19 pandemic in the digital transformation era. The selection of sources based on several criteria, namely: 1) literature must be directly related to local wisdom, character education, and the 2013 curriculum;(2) search for related literature originating from the results of research reports, national journals, international journals, relevant books, scientific articles, and scientific data related to the study of this article;(3) limitation of the year of publication of the literature in the last two years (2020-2021). The findings of the research indicate that, during the Covid-19 pandemic, the growth of digital transformation in the online learning system accelerated in Indonesia. However, this online learning brings various negative impacts on the pedagogical aspects of students in elementary schools. During the Covid-19, teachers must comprehend and use pandemic pedagogy while conducting online learning for primary school pupils. © 2021 ACM.
ABSTRACT
The Covid-19 pandemic has resulted in restrictions and even closures of business, administrative, entertainment and educational activities. In order for these activities to continue, their activities are diverted by utilizing technology. In the field of education, the learning process is transferred to an online learning system or also known as the Online system (In the Network). The purpose of this study is to examine the problems faced in the online learning process and find the causes of these problems. This study also examines the extent to which information technology plays a role in assisting the learning process. The method used in this research is descriptive analytic. The author conducts a study of research results that have been published in various national and international journals. The results in this study are recommendations on how to apply technology appropriately in online learning to further optimize results and avoid the negative impact of disproportionate use of technology. © 2021 IEEE.
ABSTRACT
The use of Internet of Things technology may have a significant impact on changes in online learning systems in the situation of the COVID-19 pandemic. Learners need a laboratory to use and master the necessary tools or components. However, this is difficult to do in a pandemic situation. The use of remote laboratories can overcome the limitations of practicum media, which is an obstacle for students in learning. This research discusses the creation of WSNMesh32 programming tutorial modules as microcontrollers using Wi-Fi-mesh network topology that can be used in Sensor and Microcontroller Practicum courses. This research aims to find out the feasibility and perception of learners towards the WSNMesh32 Programming Tutorial Module as a learning medium in pandemic times. The method used is a quantitative method with the ADDIE model (Analyse, Design, Develop, Implement, Evaluation). Participants consisted of 31 students of the Electrical Engineering Education study program of the Industrial Electronics division 2018. The study was conducted online in light of the ongoing COVID-19 pandemic situation. The feasibility test of the module is carried out by leading discussions and reviews with experts on the material to be presented. Respondents gave a positive response to the module. The results provide the conclusion that the sensor programming tutorial module that has been produced is suitable for use as a tutorial module in sensor and microcontroller practicum courses and can be used in learning in a pandemic situation © 2021 IEEE.
ABSTRACT
The Covid-19 pandemic has intensified the online learning activities, which have becoming the most crucial platforms for learning sessions. With these online learning activities, a major concern arises particularly on the security of educators and students' data. Every element in an online learning system can be a potential target of hacking or attack. Therefore, this paper examines students' awareness of data security in online learning. Google forms have been used to gather the students' feedback. Forty-two (42) students, involving the secondary school, pre-university and university students, responded to the survey questions. The results show that most of the respondents are well aware of how to keep their data safe in online learning. It is discovered that the level of awareness about data security in online learning amongst the students is commendable. The findings of this study indicate there are various ways to secure students' data especially during online learning. © 2022. All Rights Reserved.
ABSTRACT
These papers investigated the adaption of the new technology for online learning in the COVID-19 situation, giving one more choice of online applications or programs for getting new experiences with online learning and creating ideas to resolve some of the current problems from online learning in some subjects. This study focuses to utilize the appropriate and new tools to motivate an online learning system during the Covid-19 pandemic in Lao PDR. This method had more functions of 3D for simulation the places and works by using smart mobile devices, computers, and other smart tools connecting to simulate real learning in the parts of technical and visiting were used on data collected from sample size audients from faculty of agriculture, National University of Lao, Ministry of technology and communication. The user satisfaction resulted and success rate of learning show that the positive result for technology adoption with the online learning system. © 2022 IEEE.
ABSTRACT
Following the COVID-19 pandemic, as the user-base of online synchronous communication systems skyrocketed, the shortcomings of synchronous online learning systems became more visible. Any attempt to overcome these shortcomings should be considered worthwhile due to the magnitude of potential impact. Improving the quality and addressing the shortcomings of online education is more important than ever. The goal of this multidisciplinary study that lies in the intersection of the fields of Education Science and Computer Science is to address a number of challenges of online education by incorporating AI. This study focuses on developing methods and means to ethically collect and use non-verbal cues of participants of online classrooms to assist teachers, students, and course coordinators by providing real-time and after-the-fact feedback of the students’ learning-centered affective states. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
This paper describes how an anatomy and physiology laboratory class transitioned from a paper-based lab to an online learning platform that updated the curriculum to rely more on face-to-face small group collaboration and peer teaching. Student perceptions of the new format were positive, but halfway through the transition a global pandemic challenged the new instruction method. The face-to-face curriculum had to be adjusted to a virtual format that lacked in-person interaction between the instructor and the students. This switch to virtual labs had an adverse effect on both student perception and student performance in the second half of the semester. Our observations underscore the importance of creating an interactive community when teaching virtually.