Title: Analysis of intelligent test paper generation method for online examination based on UML and particle swarm optimisation

Authors: Bo Yang; Huan Xie; Kuan Ye; Huan Qin; Rong Zu; Anchang Liu

Addresses: Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' State Grid Beijing Electric Power Company, Beijing 100031, China

Abstract: In order to overcome the problems of unclear objective function and inaccurate optimal solution in traditional test paper generation methods, an online intelligent test paper generation method based on UML and particle swarm optimisation is proposed. A mathematical model of test paper generation based on UML modelling tool is established, and the objective function of test paper generation is obtained. The improved particle swarm optimisation algorithm is used to solve the objective function of test paper generation. The optimal solution of the objective function is introduced into the question bank. The test questions in the test bank are combined and imported into the online examination system to realise the intelligent test paper formation of online examination. The experimental results show that this method has strong adaptability and can achieve better performance than the traditional intelligent test paper generation method.

Keywords: UML; particle swarm optimisation; PSO; online examination; intelligent test paper generation; constrained project indicators; state object matrix.

DOI: 10.1504/IJICT.2021.114854

International Journal of Information and Communication Technology, 2021 Vol.18 No.3, pp.317 - 333

Received: 07 Dec 2019
Accepted: 31 Dec 2019
Published online: 10 May 2021 *

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