The cutting-edge technology of virtual reality has changed almost every aspect of life in e-commerce, engineering, medicine, and entertainment. This technology has also made its way to the field of education in the form of virtual laboratories. A lack of student engagement and interest towards STEM subjects is reported in the literature. Several studies have been conducted to evaluate virtual reality in education, but these studies are limited in terms of participants and subject coverage. This study aimed to assess the effectiveness of virtual laboratories to develop student’s practical learning skills for secondary school physics. For this purpose, a desktop-based virtual laboratory application was developed based on the guidelines extracted from the literature. A user study was adopted as the main research method, and it was conducted with 184 students of 4 different schools. In each school, students were divided into two groups: experimental (used the virtual laboratory application) and control (used a physical laboratory). The data were collected through an academic quiz conducted at the end of the study. The mean score of the experimental group was 7.16, compared with 5.87 for the control group. The results revealed that the students’ learning using the virtual laboratory application was better compared with the control group. Interestingly, there was no significant difference in the performance of boys and girls in both groups. The usability questionnaire was also completed by 92 students of the experimental group to assess the application interface. The mean score was 73.5 (above average) with an internal consistency of 0.76. The participants found the virtual laboratory application to be user-friendly, easy to use, and supportive in learning.

Technology plays an important role in our society, especially in the field of education. It is quite regrettable that people, particularly the deaf, still face a lot of challenges in acquiring an education. Their learning methods are different as compared to hearing people. They use Sign Language (SL) rather than natural language to communicate and learn. They are required to put a lot of effort into learning different concepts using conventional pedagogies. Therefore, there is a dire need for some assistive technology to improve their learn-ability and understandability. In the present study, 192 deaf participants aged 5–10 years were sampled from two special child institutes. The objective of the present study was to determine the effectiveness of a mobile interface through a developed mobile application for learning basic mathematical concepts using Pakistan Sign Language (PSL). The present study bridges the gap between the technology-based method and conventional teaching methods, which are used for teaching mathematical concepts using PSL. The participants were divided into two groups, that is, one group learned through conventional methods (flash cards and board) and the other group through the developed mobile application. The difference in the performance of both the groups was evaluated by conducting quizzes. The quiz results were analyzed by the Z-test and ANOVA. The findings revealed that the Experimental Group (EG) participants, who were instructed by our mobile application showed higher proficiency in the quizzes as compared to the Control Group (CG). EG participants performed better than CG by 12% in the quizzes. A gender based difference was also observed for the quiz scores. Male participants in category C (word problem learning) performed 2.7% better than females in the EG and 2.5% better in the CG. Moreover, a significant difference was also observed in the time taken by participants in both groups to complete the quizzes. CG participants took 20 min longer than EG participants to complete the quizzes. The results of the ANOVA showed that the quiz scores were directly affected by the mode of teaching used for participants in both groups.

Scheduling techniques are essential to increase resource utilization and task execution within modern computing environments. Round Robin Scheduling (RR) ensures a fair distribution of processes needing attention but often leads to inefficiencies in systems with heterogeneous tasks or different priorities due to large latency or resource usage differences. To address such problems, this paper introduces the Agent-based Adaptive Dynamic Round Robin (AADRR) process scheduling technique, which enhances process scheduling by continuously adjusting the time quantum and criteria, combining CPU burst time and priority. In the proposed AADRR, all processes are ranked dynamically by a software agent based on user preferences and current system load. This agent operates independently by keeping an eye on system parameters and making the required adjustments in real-time without requiring human intervention. We place processes in the queue according to their order of importance. A dynamic time quantum policy is suitable whenever it meets the mean duration of each process in the queue. Every round has the time quantum adjusted based on this method average burst time. AADRR highlights that the short processes are managed properly and the long processes are completed within a few rounds to fairly complete and maintain all the processes in the system. The proposed AADRR is more suitable for periodic tasks that employ a dynamic scheduling system and adapt time quantum according to the system state and job features. Additionally, AADRR efficiently manages preemptable tasks, using dynamic scheduling policies to accommodate variations in process priority and CPU burst times, ensuring fair scheduling, efficient resource utilization, and dynamic adaptability. To validate the effectiveness of the AADRR algorithm, we performed a comparative performance analysis with twelve other algorithms, including five traditional CPU scheduling algorithms and seven advanced job scheduling techniques, demonstrating optimal performance results. In our experiments, synthetic workload traces were generated using the Monte Carlo probability distribution method, which is scientifically recommended for creating diverse workload traces. Small, medium, and large datasets were used, with the small workload traces obtained from published studies and the large traces produced by the Monte Carlo simulation. AADRR efficiently reduces average turnaround times and average waiting times for each workload and performs better in response time. AADRR may not always provide the most favorable measures in all scenarios. Still, it performs better than other scheduling techniques in system performance, while being more efficient and flexible for different workloads.

Task scheduling is required to optimize the use of resources and minimize the waiting time in cloud computing systems. However, the traditional fixed scheduling techniques cannot address the variability, heterogeneity and dynamic behavior of cloud workloads. Current timesharing schedulers use fixed CPU time quantum and do not have any dynamic mechanisms to adapt to the workload, and SLA-driven priorities, resulting in poor performance of cloud infrastructures. To overcome these drawbacks, this paper proposes a novel Multi-Agent Adaptive Dynamic Round Robin (MAADRR) scheduling model for dynamic and heterogeneous cloud systems. Compared with traditional single-layer schedulers, MAADRR has hierarchical agent-based coordination. This allows it to balance workloads across the infrastructure and have local flexibility simultaneously. The framework is implemented and evaluated on the CloudSim Plus simulator and enables realistic modelling of the multi-level cloud execution environment. The VM-level agents Adaptive Round Robin are dynamically changed by real-time quantum due to real-time workload feedback. A global coordination agent, which exists at the infrastructure layer, integrates the previously set scheduling strategies, such as Dynamic Max-Min, DRALBA, PSSLB, PSSELB, and DeRALBA, to coordinate the distribution of different tasks among the heterogeneous resources. This multi-agent design is cooperative and provides autonomous local optimization, stability, and fairness of the system. The experiments were conducted on workloads of varying sizes. The experimental results obtained from Google-Cluster-like Job (GoCJ) traces demonstrate that MAADRR is a more energy-efficient solution and outperforms the baseline methods in terms of Energy Consumption, Throughput, Average Waiting Time (AWT), Average Turnaround Time (ATAT), and Total Completion Time (TCT). The findings confirm that MAADRR is a scalable scheduling framework that is suitable for cloud environments.