E-learning is regarded as a mandatory teaching and learning approach in higher education worldwide. Despite its importance and popularity, several issues on its use and effectiveness still remain. Universities are facing problems oflow e-learning usage among students and even academic staffs. This study investigate students' acceptance of e-learning in university using modified TAM model consists of six constructs namely instructor characteristics, computer self-efficacy, course design, perceived usefulness, perceived ease of use and intention to use. Results shown that computer self-efficacyhas significantly effects ease of use, while perceived ease of use significantly affectsintention to use e-learning.

Studies of educational games (EG) are rapidly growing in recent years due to its promising potential for education. New with many games produced from industry or games are still low due to issues from both student matching with syllabus as well as factors that contribute to student acceptance of EG in orde study proposed and validated games acceptance framework collected with 180 university students Learning Expectancy, Effort Expectancy, Attitud Games designers can leverage the issues concern with students

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GA) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use adaptive parameterization when applying the GA operators. In this work, adaptively parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that an adaptive approach with position factor is more effective for the proposed PSO hybrids. Compared to single PSO with adaptive inertia weight, all the PSO hybrids with adaptive probability have shown satisfactory performance in generating near-optimal solutions for all tested functions.

Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.

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