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This study seeks to address knowledge gaps regarding the role of self-regulated learning as a mediator in the relationship between interactions, internet self-efficacy, and student satisfaction. We conducted a survey of 1590 students from north Indian universities about their level of satisfaction, self-regulated learning, internet self-efficacy, and different interactions (learner-learner interaction, learner-content interaction, and learner-instructor interaction) during emergency remote teaching. By employing a two-stage SEM-ANN approach, this study contributes to methodological advancements and provides a comprehensive analysis of complex relationships. According to the findings, the identified factors are significant predictors of students' satisfaction with online education in synchronous settings. Our research also shows that self-regulated learning fully mediates the effect of internet self-efficacy on student satisfaction during emergency remote teaching. This suggests that internet self-efficacy alone may not guarantee student satisfaction unless accompanied by self-regulated learning skills. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop an attitude scale towards online teaching and learning for higher education teachers. It included 687 participants (77 Professors, 67 Associate Professors and 543 Assistant Professors) from various colleges and Universities of India. This tool development was a part of research conducted to examine the preparedness of Indian higher education teachers during the period of lockdown due to Covid-19 pandemic. After reviewing related literature, initially 37 items related with the attitude of teachers towards online education were framed. Later on, 11 items were modified and four were deleted as per the opinions of the experts. The draft scale consisting of 33 items was administered on teachers and data was collected with the help of Google forms. Item analysis was carried out using t-value and r-value. Reliability of scale was determined by using Cronbach Alpha value (0.88) and split-halt correlation (0.82). After item analysis, the scale items were reduced to 30 and four factors were established as a result of factor analysis (Principal Component Analysis). Psychometric scale analyses have shown that this scale is valid, reliable and thus can be used in the evaluation of teachers 'attitudes towards online teaching and learning. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques. [ABSTRACT FROM AUTHOR]