漸進隨機選題之最大優先指數法在電腦化適性測驗之應用與選題控制成效之檢驗

This study aimed to develop a procedure for severely constrained item selection with improving item pool usage rate in computerized adaptive testing. The authors proposed the progressive maximum priority index method (PG-MPI) to combine the maximum priority index method (MPI) with the progressive method (PG). Through a series of simulation, independent of pool size and test length, it showed that compared with the other four item selection methods the PG-MPI is able to accommodate various non-statistical constraints, lead to fewer constraint violations, provide better pool security maintaining, and increase the item bank utility simultaneously with little loss in measurement precision when the acceleration parameter is set to lower value. Finally, study limitations are noted and suggestions for future investigations are proposed.