The design of personal mobile technologies for lifelong learning
Computers & Education - VIRTUALITY IN EDUCATION selected contributions from the CAL 99 symposium
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Strategies for controlling testlet exposure rates in computerized adaptive testing systems
Strategies for controlling testlet exposure rates in computerized adaptive testing systems
The development and evaluation of a software prototype for computer-adaptive testing
Computers & Education
m-learning: a new stage of e-learning
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Expert Systems with Applications: An International Journal
An intelligent human-expert forum system based on fuzzy information retrieval technique
Expert Systems with Applications: An International Journal
Standardized course generation process using Dynamic Fuzzy Petri Nets
Expert Systems with Applications: An International Journal
Mobile learning: A framework and evaluation
Computers & Education
Genetic algorithm based multi-agent system applied to test generation
Computers & Education
Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
The design and evaluation of a computerized adaptive test on mobile devices
Computers & Education
Design and evaluation of an XML-based platform-independent computerized adaptive testing system
IEEE Transactions on Education
Self-assessment in a feasible, adaptive web-based testing system
IEEE Transactions on Education
A tabu search approach to generating test sheets for multiple assessment criteria
IEEE Transactions on Education
Visual attention for solving multiple-choice science problem: An eye-tracking analysis
Computers & Education
Expert Systems with Applications: An International Journal
Journal of Network and Computer Applications
Constructing concept maps for adaptive learning systems based on data mining techniques
Expert Systems with Applications: An International Journal
A cloud-based learning environment for developing student reflection abilities
Computers in Human Behavior
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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.