解題所需認知運作複雜程度對試題難度有合理的預測力，而認知的分析與教學因應所採用的描述性語言較為接近。換言之，認知成分的分析可以協助教師將部分的統計資訊轉化為教學設計的參考資源。因此，本文發展一個數學試題認知複雜度分析架構，並針對TASA-MAT六年級線上測驗，逐題進行試題認知成分評定，再以回歸的統計方式，實徵檢驗該認知複雜度分析架構對試題難度的解釋力。2005年與2006年兩份的TASA-MAT線上測驗，共有53題，研究中以步驟數、表徵轉化、關係推衍、情境新穎、和抽象邏輯等六個成分進行試題認知複雜度的評定，再以認知成分需求度為預測變項，採多元迴歸預測試題難度。結果顯示該認知複雜度分析架構對跨數學內容領域試題難度變異的解釋量有27%；如果進一步針對數與計算、統計與機率、或代數等不同數學內容領域進行細部的分析，一到兩項認知成分的預測力可以達五成左右。換言之，依據初步應用結果，本文所提供的認知複雜度分析架構對數學教師的教學與評量設計頗具參考價值。

Large scale assessment routinely release part of the sample items to communicate the assessment theme. Proportion correct of each release item is also included in the release documentation. To translate the statistic information into teaching practice adjustment, teachers usually need some professional supports. In this study, an analysis framework on item cognitive complexity is proposed and implemented. The 2005 and 2006 on-line tests of the Taiwan Assessment of Student Achievement in Mathematics (TASA-MAT) for the 6th graders were used for the preliminary analysis. A 6 cognitive components coding schema was developed to predict the item difficulty parameters. The results suggest that the framework proposed can predict around 27% of the difficulty variance across five contents. Within some sub-contents, one to two cognitive components can account up for 50% of the difficulty variance. The implications of these results for math teachers are discussed.