|Title||An adaptive testing system for supporting versatile educational assessment|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Huang, Y-M, Lin, Y-T, Cheng, S-C|
|Journal||Computers and Education|
|Keywords||Architectures for educational technology system, Distance education and telelearning|
With the rapid growth of computer and mobile technology, it is a challenge to integrate computer based test (CBT) with mobile learning (m-learning) especially for formative assessment and self-assessment. In terms of self-assessment, computer adaptive test (CAT) is a proper way to enable students to evaluate themselves. In CAT, students are assessed through a process that uses item response theory (IRT), a well-founded psychometric theory. Furthermore, a large item bank is indispensable to a test, but when a CAT system has a large item bank, the test item selection of IRT becomes more tedious. Besides the large item bank, item exposure mechanism is also essential to a testing system. However, IRT all lack the above-mentioned points. These reasons have motivated the authors to carry out this study. This paper describes a design issue aimed at the development and implementation of an adaptive testing system. The system can support several assessment functions and different devices. Moreover, the researchers apply a novel approach, particle swarm optimization (PSO) to alleviate the computational complexity and resolve the problem of item exposure. Throughout the development of the system, a formative evaluation was embedded into an integral part of the design methodology that was used for improving the system. After the system was formally released onto the web, some questionnaires and experiments were conducted to evaluate the usability, precision, and efficiency of the system. The results of these evaluations indicated that the system provides an adaptive testing for different devices and supports versatile assessment functions. Moreover, the system can estimate students' ability reliably and validly and conduct an adaptive test efficiently. Furthermore, the computational complexity of the system was alleviated by the PSO approach. By the approach, the test item selection procedure becomes efficient and the average best fitness values are very close to the optimal solutions.