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Immersive virtual reality (IVR) simulations for education have been found to increase affective outcomes compared to traditional media, but the effects on learning are mixed. As reflection has previously shown to enhance learning in traditional media, we investigated the efficacy of appropriate reflection exercises for IVR. In a 2 x 2 mixed-methods experiment, 89 (61 female) undergraduate biochemistry students learned about the electron transport chain through desktop virtual reality (DVR) and IVR (media conditions). Approximately, half of each group engaged in a subsequent generative learning strategy (GLS) of teaching in pairs (method conditions). A significant interaction between media and methods illustrated that the GLS of teaching significantly improved transfer (d = 1.26), retention (d = 0.60) and self-efficacy (d = 0.82) when learning through IVR, but not DVR. In the second part of the study, students switched media conditions and the experiment was repeated. This time, significant main effects favoring the IVR group on the outcomes of intrinsic motivation (d = 0.16), perceived enjoyment (d = 0.94) and presence (d = 1.29) were observed, indicating that students preferred IVR after having experienced both media conditions. The results support the view that methods enable media that affect learning and that the GLS of teaching is specifically relevant for IVR.

Although nonflexible, scaled molecular models like Pauling-Corey's and its descendants have made significant contributions in structural biology research and pedagogy, recent technical advances in 3D printing and electronics make it possible to go one step further in designing physical models of biomacromolecules: to make them conformationally dynamic. We report the design, construction, and validation of a flexible, scaled, physical model of the polypeptide chain, which accurately reproduces the bond rotational degrees-of-freedom in the peptide backbone. The coarse-grained backbone model consists of repeating amide and alpha-carbon units, connected by mechanical bonds (corresponding to phi and psi angles) that include realistic barriers to rotation that closely approximate those found at the molecular scale. Longer-range hydrogen-bonding interactions are also incorporated, allowing the chain to easily fold into stable secondary structures. This physical model can serve as the basis for linking tangible bio-macromolecular models directly to the vast array of existing computational tools to provide an enhanced and interactive human-computer interface. We have explored the boundaries of this direction at the interface of computational tools and physical models of biological macromolecules at the nano-scale. Using a CAD-biocomputational framework, we have provided a methodology to design and build physical protein models focusing on shape and dynamics. We have also developed a workflow and an interface implemented for such bio-modeling tools. This physical-digital interface paradigm, at the intersection of native state proteins (P), computational models (C) and physical models (P), provides new opportunities for building an interactive computational modeling tool for protein folding and drug design. Furthermore, this model is easily constructed with readily obtainable parts and promises to be a tremendous educational aid to the intuitive understanding of chain folding