Enhancing the assessment item rearrangement with bin packing algorithms

Abstract

Online test system has been used widely in various distance learning and training systems. As more and more intelligent online systems appeared, lots of teachers could generate items and tests by diverse e-learning tools. An assessment test contains a set of items. There exist only two typical test delivery approaches supported by most online test systems. One is that a group of items are represented in a single page, and other one is that one item is presented in a single page. In case of the former approach, we need to discuss about how many items should be represented in a single page. In other words, space utilization is an important performance measure of test delivery engine. Firstly, this thesis describes the design and implementation of the test generation module and the test delivery engine. And, we apply bin packing algorithms to typical items rearrangement method for responsive test sheet of multiple screen sizes. It enables each item not to be divided into two or more pages and each page to be filled efficiently with items. Lastly, our experiment shows our proposed method based on Best-Fit algorithm is superior to other algorithms with the number of pages spent.