以往開放系統類興趣量表的編製策略，大抵以所要區辨的類別自身為量表的多向度構念。但在環境變動愈來愈激烈下，開放式興趣量表面臨著修訂週期愈來愈短的難題，學系興趣量表即是如此。本研究提議以知識描述子取代以學系類別自身為構念之量表編製策略。在以美國大學科系主修學程分類（CIP）與O*NET職業資料庫資料進行之區別分析結果顯示，知識描述子的區別力遠高於Holland 的RIASEC興趣描述子、也高於O*NET 之技能與能力描述子，顯示此修訂策略相當可行，可作為「學系探索量表」等興趣量表修訂時之主要參考依據。

In the past, academic interest inventory always use the classified groups to be discriminated as the multi-dimensional construct. However, due to the rapidly changing higher education environment, researchers do test developing faces the difficulty that the revision cycle of scale is shortened. This paper proposes another strategy. We hypothesize
that using “knowledge descriptor” instead of “classified groups” is a better strategy.Databases of the Classification of Instructional Program (CIP) & O*NET are used to examine this proposition. Discriminant analysis shows that “knowledge descriptor”has much higher discriminating power than Holland’s RIASEC descriptor. It is also found that “knowledge descriptor” has higher discriminating power thanthe “skill descriptor”
and the “ability descriptor”. Implications of this revision strategy on academic interest inventory are further discussed.