Shape-memory polymers are a class of smart materials that have recently been used in intelligent biomedical devices and industrial applications for their ability to change shape under a predetermined stimulus. In this study, photopolymerized thermoset shape-memory networks with tailored thermomechanics are evaluated to link polymer structure to recovery behavior. Methyl methacrylate (MMA) and poly(ethylene glycol) dimethacrylate (PEGDMA) are copolymerized to create networks with independently adjusted glass transition temperatures (T-g) and rubbery modulus values ranging from 56 to 92 degrees C and 9.3 to 23.0 MPa, respectively. Free-strain recovery under isothermal and transient temperature conditions is highly influenced by the Tg of the networks, while the rubbery moduli of the networks has a negligible effect on this response. The magnitude of stress generation of fixed-strain recovery correlates with network rubbery moduli, while fixed-strain recovery under isothermal conditions shows a complex evolution for varying T, The results are intended to help aid in future shape-memory device design and the MMA-co-PEGDMA network is presented as a possible high strength shape-memory biomaterial.