Modelling a digital graphic novel

Researchers and designers proposed “digital graphic novels” (DGN) because reading a graphic novel from physical book is not a popular choice anymore due to the absence of interactivity, user control and rich multimedia support. It however opens ups debates on whether such graphic narratives is usable as well as effective for transferring knowledge. Researchers in Human-Computer Interaction field argue that this kind of media enables the absorption of previous knowledge in the story presented and can suggest new knowledge to the user. However, not designing DGN based on a solid model will continue to bring prejudice for this media. This paper focuses specifically on cognitive processes of constructing a conceptual DGN model of an explanatory type. Utilising action research cycle, Rigaud's unsupervised framework of DGN, Kane's educational graphic narrative model, Hughes's dual pathways model, and Cohn's visual graphics narrative model were examined and analysed to extract variables that can explain DGN semantics. The analysis revealed that the unsupervised framework can be extracted to improve newer model. We thus enhance Kane's educational graphic narrative model in order to translate the knowledge into a target system. Expert evaluation was carried out on the enhanced model. The evaluation results provide positive insights specifically on idealising assumptions and viability of the model. We then proposed the final premise where visual narrative comprehends the meanings of sequential images characterised by order and timing delivered in the storytelling strategised by graphic narrative and graphic text consisting of images, content and print texts in the knowledge base. This premise of the model serves as one of the principle instruments of modern science, with specific contribution to the development of usable DGNs. The model contains rational attributes to be used as a foundation for DGN design and development.