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The challenge of optimizing personalized learning pathways to maximize student engagement and minimize task completion time while adhering to prerequisite constraints remains a significant issue in educational technology. This paper applies the Salp Swarm Algorithm (SSA) as a new solution to this problem. Our approach compares SSA against traditional optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results demonstrate that SSA significantly outperforms these methods, achieving a lower average fitness value of 307.0 compared to 320.0 for GA and 315.0 for PSO. Furthermore, SSA exhibits greater consistency, with a lower standard deviation and superior computational efficiency, as evidenced by faster execution times. The success of SSA is attributed to its balanced approach to exploration and exploitation within the search space. These findings highlight the potential of SSA as an effective tool for optimizing personalized learning experiences 

Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan. 

This paper presents a work-in-progress on a learning system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skilled robotics instructors, particularly in community colleges and the expensive demand for training equipment. The study of robotics at the college level represents a wide range of interests, experiences, and aims. This project works to provide students the flexibility to adapt their learning to their own goals and prior experience. We are developing a system to enable robotics instruction through a web-based interface that is compatible with less expensive hardware. Therefore, the free distribution of teaching materials will empower educators. This project has the potential to increase the number of robotics courses offered at both two- and four-year schools and universities. The course materials are being designed with small units and a hierarchical dependency tree in mind; students will be able to customize their course of study based on the robotics skills they have already mastered. We present an evaluation of a five module mini-course in robotics. Students indicated that they had a positive experience with the online content. They also scored the experience highly on relatedness, mastery, and autonomy perspectives, demonstrating strong motivation potential for this approach. 

Virtual Reality (VR) has existed for many years; however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this work we present a brief history, current research areas, and areas for improvement in virtual reality 

Due to the rapidly evolving nature of the Virtual Reality field, many frameworks for multiuser interaction have become outdated, with few (if any) designed to support mixed virtual and non-virtual interactions. We have developed a framework that lays an exten- sible and forward-looking foundation for mixed interactions based upon a novel method of ensuring that inputs, visuals, and networking can all communicate without needing to understand the others’ internals. We tested this framework in the development of several applications and proved that it can easily be adapted to support application requirements it was not originally designed for.