Item Classification Algorithm for Computer Adaptive Testing Based on Web Services

A learning management system (LMS) plays an important role in e-learning. It provides educational services to manage learner profiles and learning contents. One of the core processes of an LMS is the computer adaptive testing (CAT), which helps teachers evaluate student’s knowledge capability. Although many CAT systems have been introduced to LMSs, they do not provide the efficient item classification algorithm that supports interoperability. Such interoperability makes systems feasible to efficiently interact with each other. Thus, this paper proposes a CAT framework supporting interoperability by using web service. In this framework, the service of the CAT system is to generate an optimal item set for an item selection module of a CAT web application. For this reason, an item classification module is redesigned and implemented by a web service. Inside this module, there are two significant components for cooperation. The former is an item classification algorithm proposed by means of a triangle-decision tree (TDT) collaborating with genetic algorithms (GAs) for generating the optimal item set, whereas the latter is the web service for making the optimal item set interoperable. The simulation results show that the item classification module can classify accurately item sets and the web services can work properly.