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In this paper, we present a new item response model for computerized adaptive testing: Item Response Theory combined with Elo rating. Adaptive test systems require a calibrated item bank and item calibration methods are usually based on Item Response Theory (IRT). However, these methods require item pretesting on large sample sizes, which is very expensive. Hence, this paper presents alternative methods for item difficulty calibration.Results show that combining IRT with Elo rating is an alternative model for adaptive item sequencing, which offers not only estimations for abilities, but for item difficulties too. The new adaptive item sequencing model was compared with IRT on artificial data. Results show that the new method is able to estimate the ability of the examinee, although more items are required compared to IRT. Hence, this method is recommended for test systems where adaptation to the user knowledge level is a requirement, but the duration of the measurement is less important, i.e. practice systems.

2010 Mathematics Subject Classification. 97Q70.1998 CR Categories and Descriptors. K.3 [Computers and Education]: Computer and Information Science Education [Computer science education].

Key words and phrases. Item Difficulty, Item Response Theory, Performance Assessment.