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

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.

[1]  Jun Zhang,et al.  An Intelligent Testing System Embedded With an Ant-Colony-Optimization-Based Test Composition Method , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Bertrand M. T. Lin,et al.  On the development of a computer-assisted testing system with genetic test sheet-generating approach , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Kun Hua Tsai,et al.  Dynamic computerized testlet-based test generation system by discrete PSO with partial course ontology , 2010, Expert Syst. Appl..

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[6]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988, Wiley interscience series in discrete mathematics and optimization.

[7]  Gwo-Jen Hwang,et al.  An innovative parallel test sheet composition approach to meet multiple assessment criteria for national tests , 2008, Comput. Educ..

[8]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[9]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[10]  Yannis Manolopoulos,et al.  R-Trees: Theory and Applications , 2005, Advanced Information and Knowledge Processing.

[11]  Gwo-Jen Hwang,et al.  A test-sheet-generating algorithm for multiple assessment requirements , 2003, IEEE Trans. Educ..

[12]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988 .

[13]  Gwo-Jen Hwang,et al.  Multi-Objective Parallel Test-Sheet Composition Using Enhanced Particle Swarm Optimization , 2009, J. Educ. Technol. Soc..

[14]  Cheng-Jian Lin,et al.  Test-Sheet Composition Using Immune Algorithm for E-Learning Application , 2007, IEA/AIE.

[15]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[16]  Andrea Schaerf,et al.  A Survey of Automated Timetabling , 1999, Artificial Intelligence Review.

[17]  Gwo-Jen Hwang,et al.  A tabu search approach to generating test sheets for multiple assessment criteria , 2006, IEEE Trans. Educ..

[18]  David K. Smith.  Theory of Linear and Integer Programming , 1987 .

[19]  L. Bodin.  ROUTING AND SCHEDULING OF VEHICLES AND CREWS–THE STATE OF THE ART , 1983 .

[20]  Quan-Ke Pan,et al.  A novel online test-sheet composition approach for web-based testing , 2009, 2009 IEEE International Symposium on IT in Medicine & Education.

[21]  Bertrand M. T. Lin,et al.  An effective approach for test-sheet composition with large-scale item banks , 2006, Comput. Educ..