Computer Adaptive Testing and Cloud Computing

Abstract

Computer Adaptive Testing (CAT) is an example of a Computer Based Test (CBT) and is one of the main trending topics in the area of knowledge testing and, more recently, in e-learning or in Intelligent Tutoring Systems scenarios. The Item Response Theory (IRT) defines the theoretical basis of a CAT implementation, which assumes the existence of a repository of properly calibrated items that is used during the testing process of a particular examinee. The calibration and adaptation are based on an Item Characteristic Curve (ICC) related to an specific model, being Rasch's models the most widely used. CAT systems require high computational cost to implement the calibration and evaluation processes and the amount of concurrent users at a time could be large enough. Thus, the platform must support high concurrency and availability to perform a desired level of functionality. Technological tendencies in computing offer each time better platforms to develop and manage big collections of data for its processing and relevant information extraction. This paper presents a perspective of using new technologies in CAT as an alternative of implementation. Particularly, the use of a cloud computing platform as current alternative for online CAT systems using the capabilities of multicore processing and big amount of RAM that offers the cloud, to resolve the proper mathematical equations related to psychometric models and the operations described in their algorithms in a real evaluation scheme.