Computerized Adaptive Testing (CAT) is becoming a prevalent form for large-scaled educational tests. In CAT, each student encounters a unique test in which items are adaptively selected based on his/her responses to previous questions. The traditional information-based item selection method has created a number of problems, including high test-overlap rate and substantially skewed item exposure distribution. On a different approach, Chang and Ying (1999) proposed the a-stratified design (STR) and advocated the use of low discriminating items in the earlier stages of testing. Research findings have indicated that this method is effective in achieving a balanced utilization of the entire item pool and reducing test-overlap rate, without sacrificing the efficiency in ability estimation. Nevertheless, this new approach has not taken into consideration the many practical situations in which non-statistical constraints are necessary. This paper reviews existing models that tackle non-statistical constraints of various complexities. Building on these models, the paper proposes three approaches on how to incorporate non-statistical constraints in the STR designs. The strengths and weaknesses of these methods as well as problems in implementations are also discussed. Copyright © 2003 Hong Kong Institute of Education Research, The Chinese University of Hong Kong.
|Journal||The Official Journal of Global Chinese Society FOR Computers in Education|
|Publication status||Published - 2003|
CitationLeung, C. K., Chang, H. H., & Hau, K. T. (2003). Making α-stratified computerized adaptive testing design more practical: Imposing non-statistical constraints. The Official Journal of Global Chinese Society FOR Computers in Education, 1(1), 64-86.
- Alt. title: 加入非統計類限制以強化電腦自適測試「分層遞增a法」的實用性