ABSTRACT
With the development of computer and information technology, mobile teaching has enjoyed pride of place among teaching mediums in the past two decades. To visually explore the mobile learning hotspots and trends present in international journals, this study adopted two science mapping tools (CiteSpace and VOSviewer) to first detect and then visualise emerging trends (i.e., hotspots) in the mobile learning literature. A total of 528 mobile learning articles published between 2003 and 2021 that appeared in 21 international educational technology journals indexed in the SSCI database were retrieved for bibliometric analysis. The results show (1) there was a remarkable increase in academic output in this field starting in 2008 that topped out in 2021;(2) co-authorship with academics from diverse countries/regions and institutions was evident;(3) three trending foci in the literature include defining mobile learning, designing learning systems, and exploring mobile learning effectiveness;and (4) the high-frequency co-cited publications focus on the effectiveness of mobile devices via different research methods. This study provides scholars with an accessible summary of the current trends in mobile learning, identifies the active researchers in this field, and reports on which outlets are most relevant for research produced on this topic. In addition, the findings have direct implications for the education and private sectors. Mobile devices are not widely adopted in classroom settings and are often considered a learning tool more suited for out-of-class assignments or practice. Therefore, it is necessary for information technology educators to invest in actively initiating the integration of mobile technology into the classroom. Those in the technology industry should aim to develop mobile devices and relevant educational applications/software that can be utilised not only within the confines of the classroom but also to bridge in-class and out-of-class learning.
ABSTRACT
Chemistry education has been affected in various aspects, especially teaching models, teaching technology, curriculum design, and teaching assessment, in Chinese Mainland since COVID-19 struck. To explore the change in teaching performance and differences in those teaching performances between middle school teachers and university teachers, this study analyzes 46 chemical education journal articles in three academic levels, including Junior High School (JHS), Senior High School (SHS), and university through content analysis method. The result shows that all levels of chemistry teachers (CTs) prefer to adopt two teaching models (live teaching or a combination of live and recorded teaching) and use Tencent Meeting as a teaching platform, but, university chemistry professors can integrate some software (MOOC and WeiXin as well as QQ) into their class to improve the effectiveness of teaching. As for curriculum design and teaching assessment, university teachers can employ the news in the context of the COVID-19 pandemic to carry out teaching more deeply than middle school teachers because university students learn more professional chemistry knowledge, but, all of them tend to evaluate students promptly (like oral assessment) through classroom interaction. Thus, it suggests the government and the school develop educational applications/software and teaching resources and a diverse curriculum to enhance online teaching work both domestically and foreign. And, it is also suggested that teachers should actively participate in the training of educational technology and strengthen cooperation with teachers in other disciplines to build interdisciplinary classrooms.
ABSTRACT
We consider the problem of partitioning a set of items into unlabeled subsets so as to optimize an additive objective, i.e., the objective function value of a partition is equal to the sum of the contribution of each subset. Under an arbitrary objective function, this family of problems is known to be an N P -complete combinatorial optimization problem. We study this problem under a broad family of objective functions characterized by elementary symmetric polynomials, which are "building blocks” to symmetric functions. By analyzing a continuous relaxation of the problem, we identify conditions that enable the use of a reformulation technique in which the set partitioning problem is cast as a more tractable network flow problem solvable in polynomial-time. We show that a number of results from the literature arise as special cases of our proposed framework, highlighting its generality. We demonstrate the usefulness of the developed methodology through a novel and timely application of quarantining heterogeneous populations in an optimal manner. Our case study on real COVID-19 data reveals significant benefits over conventional measures in terms of both spread mitigation and economic impact, underscoring the importance of data-driven policies. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Objective To develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by "health information technology” and "neural network image recognition”, so as to give full play to the advantages of "grid management”. This system is applied to the normalized prevention and control of COVID-19 epidemic. Methods The model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. Results The proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. Conclusions Antigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.
ABSTRACT
Coronavirus disease (COVID-19) has spread all over the world and has impacted tourism globally, with countries taking various measures such as travel restrictions, border closures, lockdowns, or quarantines to contain the virus. Tourists' motivation has also been affected by COVID-19, but so far, the literature has not yet discussed their concern over COVID-19 as well as the relationships among their motivation, involvement, and behavior intention. Therefore, this study fills the gap in the literature by taking cycling tourism as an example to understand the involvement of tourists concerning COVID-19 and presents the depth and breadth of its effects upon tourism. Due to the challenge of face-to-face, on-site investigation, we employ an online survey for data collection, use exploratory factor analysis to extract the main factors of motivation, involvement, and behavior intention, and set up a structural equation model to examine the relationships among the three factors. The results show that COVID-19 has positively and significantly affected motivation and involvement. Motivation positively and significantly affects involvement, and involvement affects motivation and behavior intention. The main finding herein is that motivation does not affect behavior, but involvement does mediate between the motivation and behavior of cyclists during COVID-19. Therefore, people may perceive the risk of health and wellbeing through such involvement.
ABSTRACT
The practice and research on online chemistry education have received great attention in the context of the COVID-19 pandemic worldwide. This paper describes the teaching practice of online learning based on three online courses for pre-service chemistry teachers. To adapt online teaching requirements, we reshaped the structure of the physical chemistry course, for example, adjusting the assessment approaches, and reinforcing long-distance instruction. Besides, we also shared the course of training chemistry teaching skills online publicly, and provided multiple learning approaches after class. Moreover, we introduced chemistry education research based on website data in an educational research methods course and shared ideas through the technology platform.