Design And Realization Of Intelligent Test Paper Generating System

Intelligent test paper generation system is the generation of test papers by computer technology and intelligent algorithm. The use of intelligent test paper system can reduce the workload of Teachers that propose test paper. At the same time compared with other test methods, using intelligent searching and matching test strategy maximumly generates test paper that meets the requirements of the teacher, while achieves the scientific evaluation of the candidates while ensuring the fairness of the test. Test method: strategy is the key to intelligent test system, but the test methods currently used more often is very difficult to meet the actual requirements of users, so designing a suitable and efficient test paper strategy that meet the user equirements is of practical significance to the education of things.Intelligent test paper is the problem of multi-objective parameter some constraint conditions of optimization problem, commonly used algorithms such as random algorithm, ant colony algorithm, genetic algorithm and other algorithms, with test speed,generation of test paper quality is not high, the success rate is low. In order to solve these problems, this paper studies the application of hybrid particle swarm optimization algorithm in generating test paper and the main work of this paper includes:(1) This paper expounds the relevant theory knowledge of intelligent test paper generation, and introduces the application of random algorithm and improved genetic algorithm in intelligent test paper composition problem.(2) Based on in-depth analysis on the basic attributes of the generation of test paper quality evaluation criteria, basic principles of test questions and basic principles,combined with items such as item types, degree of differentiation, knowledge, cognitive level of comprehensive consideration, the constraint condition of intelligent test paper generation is determined and through the mathematical model and objective function of the intelligent test paper optimization problem optimizes test paper.(3) In this paper, a hybrid particle swarm optimization (PSO) algorithm is proposed, by introducing crossover operation and mutation operation in the standard particle swarm optimization algorithm. By comparing the simulation data with other test paper algorithm, the efficiency of the test paper generation strategy is illustrated.(4) In this paper, the improved genetic operators are introduced, and the multi point mutation strategy for the crossover operator is proposed.(5) Based on the analysis of the function of the system, a set of intelligent test paper evaluation system is designed and realized based on the MFC tool and the algorithm of generating test paper, at the same time the realization process of each function module is described.