A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm

Abstract

Abstract. In e-learning environment, online testing system can help to evaluate students' learning status precisely. To meet the users' multiple assessment requirements, a new test-sheet composition model was put forward. Based on the proposed model, a genetic algorithm with effective coding strategy and problem characteristic mutation operation were designed to generate high quality testsheet in online testing systems. The proposed algorithm was tested using a series of item banks with different scales. Superiority of the proposed algorithm is demonstrated by comparing it with the genetic algorithm with binary coding strategy.

A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

“…Many evolutionary algorithms are used for test combinations Huang et al, 2009;Lee et al, 2007;Wang et al, 2009). Cheng et al (2009) used a PSO algorithm to select tailored questions for each learner from a large-scale item bank and simultaneously satisfy multiple assessment requirements, such as the exposure frequency of questions, the relevant topics of the current examination, the weight of each topic, and the difficulty level of a test item.…”