The abstraction of free-standing metadata describing learning objects is typified by an analytical model which primarily focuses on the encoding of discrete properties pertaining to the learning object itself. This process often neglects the contextual properties derived from the situations in which the learning object can be used optimally. We report the derivation of situated properties for learning objects through the use of the CASE framework and explore how these properties are best encoded.