Pros and cons gamification and gaming in classroom

The aim of the current work is to assess the challenges that gamification in education are facing nowadays. Benefits and disadvantages of using gamification in classroom are both discussed to offer a clearer view on the impact of using gamification within learning process. Exploratory study cases are provided to investigate the relation between motivation and engagement of the students and gamification in training. Following this idea, a survey was conducted to assess how students behavior and motivation is affected by introducing a single, specific gamification element during a semester learning process. To stimulate competition among students, a ranking type plugin was introduced within the university learning management system used for extramural education. The results prove that motivation decreases by comparison to the previous semester.Comment: 7 pages, 3 figure

Computer Aided Education System SuperTest. Present and Prospective

This paper analyzes the testing and self-testing process for the Computer Aided Education System (CAES) SuperTest, used at the Academy of Economic Studies of Chisinau, Moldova and recently implemented at the University of Bacau, Romania. We discuss here the future of this software, from the Information Society and Knowledge Society point of view.computer aided, education, knowledge

A System for Learning Financial Accounting Bases

This paper describes a method for teaching students financial accounting bases. The method is based on correcting some typical errors, which can be observed at the beginners

BRAIN Journal - A System for Learning Financial Accounting Bases

ABSTRACT This paper describes a method for teaching students financial accounting bases. The method is based on correcting some typical mistakes, which can be observed at the beginners

Embryonic genetic algorithm with random generational growing strategy for optimizing variable ordering of BDDs

This paper addresses the problem of optimizing the variable ordering in Binary Decision Diagrams (BDDs). A new hybrid embryonic genetic algorithm is proposed for optimizing the variable ordering that combines a branch & bound technique with the basic genetic algorithm. It uses fitness based on a lower bound and embryos instead of full chromosomes. A novel growing technique introduces two new growing operators. The results of an experimental evaluation demonstrate the efficiency of the approach

Functional Problems and Maintenance Operations of Hydraulic Turbines

The exploitation in good conditions of the hydroelectric power plant imposes a rigorous maintenance of equipment and operating facilities, primarily of the turbine. The efficiency of the turbine is strongly affected by any defects which could occur during the operation. The paper makes a synthesis of the most frequent failures which have occurred during the functioning of Kaplan turbines plant and the required maintenance plan that has to be adopted. The maintenance rules for the optimal working of these turbines are also emphasized

Optimizing Variable Ordering of BDDs with Double Hybridized Embryonic Genetic Algorithm.

This paper presents a new double hybridized genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The first hybridization adopts embryonic chromosomes as prefixes of variable orders instead of complete variable orders and combines a branch & bound technique with the basic genetic algorithm. The second hybridization is done with the existing sifting algorithm, known as one of the most effective heuristic for this problem, which is incorporated as a hypermutation operator

Optimizing Variable Ordering of BDDs with Double Hybridized Embryonic Genetic Algorithm.

This paper presents a new double hybridized genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The first hybridization adopts embryonic chromosomes as prefixes of variable orders instead of complete variable orders and combines a branch & bound technique with the basic genetic algorithm. The second hybridization is done with the existing sifting algorithm, known as one of the most effective heuristic for this problem, which is incorporated as a hypermutation operator