Computerized Adaptive Mastery Tests as Expert Systems

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Abstract
Expert systems can be used to aid decision making. A computerized adaptive test is one kind of expert system, though not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines uncertain inference in expert systems with sequential probability ratio test stopping rules. Two versions of EXSPRT were developed, one with random selection of items (EXSPRT-R) and one with intelligent selection (EXSPRT-I). Two empirical studies were conducted in which these two new methods were compared to the traditional SPRT and to an adaptive mastery testing (AMT) approach based on item response theory (IRT). The EXSPRT-I tended to be more efficient than the AMT, EXSPRT-R and SPRT models in terms of average test lengths. Although further research is needed, the EXSPRT-I initially appears to be a strong alternative to both IRT- and SPRT-based adaptive tests for making categorical decisions about examinee mastery of single instructional objectives. The EXSPRT-I is clearly less complex than IRT, both conceptually and mathematically. It also appears to require many fewer examinees to establish empirically a rule base when compared to the large numbers required to estimate parameters for item response functions in the IRT model.

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