E-learning and Benchmarking Platform for Parallel and Distributed Computing

Gušev, Marjan; Ristov, Sasko; Velkoski, Goran; Ivanovska, Bisera

doi:10.3991/ijet.v9i2.3215

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Gusev et al [12] developed an e-Learning and benchmarking platform for distance learning of the Parallel and Distributed Processing course. They proposed a cloud model for the platform, as well as [13].…”

Section: Related Workmentioning

confidence: 99%

Cloud based laboratory for distance education

Ristov

Gušev

Donevski

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Today's dynamic life has changed the traditional teaching from the classroom and laboratory based classical learning to an approach using online and distance e-Learning. Universities usually follow this trend and enable their resources as online utilities for the students. There are several ways to offer these facilities online and cloud is the most promising one when analyzing several criteria. The cloud technology provides many benefits reducing the overall cost and improving the business continuity. In this paper we present an idea how to transfer the traditional computer laboratory into a cloud based utility using open source cloud frameworks. This transformation creates a dynamic, flexible, elastic, reliable and modern laboratory that can serve the students as a tool for distance learning for many courses simultaneously. It acts on a "Lab as a Service" principle.

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Section: Related Workmentioning

confidence: 99%

Cloud based laboratory for distance education

Ristov

Gušev

Donevski

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Self Cite

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“…The algorithm first generates a group of random particles, and then finds the optimal solution through iteration. In iteration, the particles adjust the flight velocity of the particles dynamically through their own and peer's flight experience, so that the whole group can fly to a better search area [14].…”

Section: B Standard Pso Algorithmmentioning

confidence: 99%

A Hierarchical Teaching Mode of College Computer Basic Application Course Based on K-means and Improved PSO Algorithm

Zhang

Lin

Zhang

2016

Int. J. Emerg. Technol. Learn.

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Abstract-As different students have different basics in learning CollegeComputer Basic Application Course, so uniform teaching methods and curriculum cannot satisfy the needs of all of the students. To address this problem, an algorithm of student clustering which can achieve hierarchical teaching is designed in this paper. After analyzing the disadvantages of slow convergence in the late processing and the local extreme of PSO, an improved Particle Swarm Optimization (i-PSO) algorithm based on granules and maximum distances is proposed. By adopting tactics of linearly decreasing weight and random distribution, adding the extremum disturbance operator, and optimizing the individual extremum of particles, the i-PSO algorithm can quickly converge to an optimal global solution�The i-PSO algorithm combined with the K-means algorithm can improve the poor clustering effect and instability of the Kmeans algorithm caused by random initial clustering center. Finally, the i-PSO and K-means algorithms are applied to the clustering. The results of simulation experiments show that this algorithm has higher accuracy, a faster convergence rate and greater stability, and can better help to realize layered teaching in College Computer Basic Application Course.

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“…These factors limit the development of the Chinese education, especially the overseas Chinese education. Therefore, it is of great significance to conduct studies on the reception psychology, reception rules and learning motivation of the oversea Chinese learners who are different in learning stages, groups and backgrounds [3,4]. The results of studies should become an inexhaustible driving force for the sustainable development of oversea Chinese education.…”

Section: Introductionmentioning

confidence: 99%

Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

Huang

2016

Int. J. Emerg. Technol. Learn.

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Abstract-The rapid updates of the resources and media in the big data age provide new opportunities for oversea Chinese education. It is an urgent task to effectively use the big data to boost the development of oversea Chinese education. However, very few studies are conducted in this area. MapReduce is a programming model of cloud computing used for the parallel computing of the large-scale data sets and this model enables programmers to run their own programs in the distributed system. In this paper we proposed a personalized overseas Chinese education model based on MapReduce mechanism , which can analyze the behavioral habits and personal preferences of users from a large pool of Chinese educational resources. In this way, the customer needs can be accurately grasped and their favorite resources are recommended from huge amounts of resources. The proposed model has a good application prospect for overseas Chinese education .

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E-learning and Benchmarking Platform for Parallel and Distributed Computing

Cited by 12 publications

References 19 publications

Cloud based laboratory for distance education

Cloud based laboratory for distance education

A Hierarchical Teaching Mode of College Computer Basic Application Course Based on K-means and Improved PSO Algorithm

Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

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