An efficient multi-objective optimization approach for Online Test Paper Generation

Abstract

With the rapid growth of the Internet and mobile devices, Online Test Paper Generation (Online-TPG) is a promising approach for self-assessment especially in an educational environment. Online-TPG is challenging as it is a multi-objective optimization problem that is NP-hard, and it is also required to satisfy the online generation requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms generally require long runtime for generating good quality test papers. In this paper, we propose an efficient multi-objective optimization approach for Online-TPG. The proposed approach is based on the Constraint-based Divide-and-Conquer (DAC) technique for constraint decomposition and multi-objective optimization. In this paper, we present the proposed DAC approach for Online-TPG and its performance evaluation. The performance results have shown that the proposed approach has outperformed other TPG techniques in terms of runtime efficiency and paper quality.