Background The current study examines student satisfaction with e-learning, the adaption of online learning channels, digital competency of students' involvement, and academic achievement during COVID-19. Purpose The purpose of this study is to examine the online influencing components for learning among University students in Pakistan during the COVID-19 Pandemic. Methods The study population comprised Pakistani University students in Punjab province who tooke online lessons throughout the epidemic. In accordance with the study's purpose, a questionnaire survey was employed to gather primary data. SPSS-23 is used for analyzing the demographic data, and cleaning and preparing data for testing hypotheses. SmartPLS 3.0 was used to investigate the suggested study framework using structural equation modeling (SEM). Results The analysis of the SEM model shows that all planned hypotheses (Adaptation of Online Education Channels -> Satisfaction about E-learning, COVID-19 Pandemic -> Adaptation of Online Education Channels, COVID-19 Pandemic -> Digital Competence, COVID-19 Pandemic -> Motivation for Online Learning, COVID-19 Pandemic -> Willingness for Online Learning, Digital Competence -> Satisfaction about E-learning, Motivation for Online Learning -> Satisfaction about E-learning, Satisfaction about E-learning -> Academic Achievement, Willingness for Online Learning -> Satisfaction about E-learning) are confirmed. Conclusion The results linked e-learning satisfaction to academic success and Pakistani students who utilized e-learning throughout the outbreak reported higher levels of academic satisfaction and achievement.

/pdf/covid-19-students-satisfaction-about-e-learning-and-academic-12hftzx6.pdf

COVID-19, students satisfaction about e-learning and academic achievement: Mediating analysis of online influencing factors

Background Learning digital technologies in higher education is a process of knowledge generation, and the rapid growth of technology in education has a significant impact on students’ learning behaviors, motivation, and knowledge development. Pakistan’s remarkable technological breakthrough has increased in the education field. Study objectives The study focuses on estimating students’ learning behaviors, identifying the positive influence of educational apps on digital learning platforms, and analyzing their impact on students’ motivation and knowledge development. Materials and methods According to the study’s objectives, a questionnaire survey was conducted to gather the primary data. The participants were students of universities in Lahore city of Pakistan. For this study, the sample size was N = 300, carefully chosen using the purposive sampling technique. Of the respondents, there were 146 male and 154 female students, and the sample consisted of individuals aged 25–35 years. Smart-PLS-Bootstrapping, T-Values (PLS) 3.2.9 and the structural equation model (SEM) were applied to get the appropriate outcomes from the proposed study framework. Results SEM analysis results shows that all proposed hypotheses [Animated Movies (AM) –> Student Motivation (SM), Educational Apps (EA) –> Knowledge Development (KD), Learning Behavior (LB) –> Animated Movies, Learning Behavior –> Educational Apps, Learning Behavior –> Knowledge Development, Learning Behavior –> Virtual Classrooms (VCr), Virtual Classrooms –> Knowledge Development, Virtual Classrooms –> Student Motivation] are confirmed while Learning Behavior –> Student Motivation is not confirmed. Conclusion This study found that digital learning platforms significantly impact students’ learning and what motivates them to learn. The study also found that using educational apps and virtual classrooms more often helps students learn more and be more motivated to learn.

/pdf/learning-behavior-digital-platforms-for-learning-and-its-264fcnjd.pdf

Learning behavior, digital platforms for learning and its impact on university student’s motivations and knowledge development

This study examines the effectiveness of animated movies (AM) in increasing students’ vocabulary and determines what students think about English language learning in Pakistan. Particularly in the education field, vocabulary is an essential factor for foreign language learning in academic achievements. This research aimed to determine whether using animated images/movies aid students in learning new vocabulary by helping them to make connections between the words they know and real-world occurrences. The research used pre-tests and post-tests to examine the impact of AM on vocabulary acquisition. Total participants ( N = 64) were 12th-grade students in Lahore, Pakistan, separated into controlled and experimental groups to make a judgment. In the second semester of college, half the students ( N = 32) utilized AM to acquire English vocabulary, while the control group employed the conventional technique. The results were evaluated using a t-test for an independent sample to detect significant differences between the groups. The vocabulary exam was constructed and verified as a pre and post-test. The findings revealed a statistically significant difference in receptive vocabulary, productive vocabulary, and overall score in favor of the experimental group owing to the use of (AM). It added to the growing body of information by investigating how animated videos might support EFL education. 

The Impact of Using Animated Movies in Learning English Language Vocabulary: An Empirical Study of Lahore, Pakistan

In metropolitan areas, traffic jams on city streets are a major source of annoyance and financial losses.Recent advancements in data processing algorithms and the widespread availability of traffic detectors have made it possible to implement data-driven strategies for reducing traffic congestion.In order to benefit from intersection cooperation in this setting, this paper presents a distributed control strategy based on RL.In this scenario, traffic prediction software's embedding that takes into account the state of nearby junctions is used to synthesize an RL controller that controls the traffic lights.Loop detector characteristics are insufficient for precise data imputed in sophisticated traffic control systems.Most current imputation methods only use these extracted characteristics, which leads to the creation of data replicas that lack the necessary precision.The clean data are first given a statistical multi-class label, with classes ranging from C1 to Cn.Then, using a deep recurrent neural network (RNN) model, the best data model is created from the labelled spotless data and applied to the class of models in the missed-volume data.Results from simulations using TRANSYT demonstrate that the suggested strategy outperforms conventional methods in terms of waiting times and other important presentation indices. 

A Deep Reinforcement Learning-Based RNN Model in a Traffic Control System for 5G-Envisioned Internet of Vehicles

Online teaching and learning in higher education during COVID-19: International perspectives and experiences

https://link.springer.com/content/pdf/10.1007/978-3-030-46951-1.pdf?pdf=button

Applied pedagogies for higher education: Real-world learning and innovation across the curriculum

Language teacher agency

Road traffic efficiency may be improved by developing an integrated network for cooperative driving provision (CDP) among vehicles on the road. In a dance urban traffic scenario, the quality of service may be compromised when required bandwidth is not provided by the existing network architecture that impacts negatively on the cooperative behaviour of the vehicles. To solve these problems, a network architecture with better scheduling of resources that meet the quality of service (QoS) is required. So, we proposed a fifth-generation (5G) technology-based network architecture that provides the required QoS for CDP. The proposed architecture consists of a resource scheduling mechanism within the 5G network and a scheme for cooperative behaviour among the vehicles on the road. The implementation of the proposed network architecture is done by incorporating it into a well-known vehicular clustering algorithm. The performance of the proposed architecture is compared with existing schemes that show improvement. 

Quality of service enabled resource scheduling for cooperative driving provision in the internet of vehicles

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Applied pedagogies for higher education: Real-world learning and innovation across the curriculum

Language teacher agency

Quality of service enabled resource scheduling for cooperative driving provision in the internet of vehicles