We present a new methodology aimed at the design and implementation of a framework for sketch recognition enabling the recognition and interpretation of diagrams.
Category Theory, a branch of modern mathematics, relies on a proof technique known as "diagram chasing" that relies entirely on the creation and interpretation of diagrams in which spatial locations indicate mathematical relations.
Research on the potential role of animation in understanding cladograms has found that animations can influence the perception and interpretation of diagrams, but that interpretation is also impacted by a user's prior knowledge and common evolution narratives (Matuk 2008a, b, c, 2010; Matuk and Uttal 2012).
Given that our sample included a wide range of positions from clinicians to senior health care administrators, our results suggest that both methods are appropriate with populations that have no particular experience with the production or interpretation of diagrams.
The semistructured interview process used a series of questions to probe the subjects' conceptual knowledge prior to viewing the diagram (Phase 1), their reasoning processes and conceptual understandings during interpretation of the diagram presented to them (Phase 2), and their evaluation and critique of the ER and its utility for their learning (Phase 3).
These findings were compared to the results from the interpretation of the diagram.
An interpretation of this diagram: thrombosis can be an effect of pregnancy and it can also be an effect of some of the ingredients of birth control pills.
Written results were replicated during think-aloud interviews with 20 students (first-year through third-year students) from institution A. Subjects were shown the representation of the central dogma and asked to explain their interpretation of the diagram as a whole, as well as any individual processes it included.
Interpretation of these diagrams is discussed, some of which are applicable in general to any statistical design of experiment.
Considering the interpretation of FORC diagrams, the magnetic mineral is primarily the mixture of PSD and SD titanomagnetite grains.
Whilst this approach is understandable on the basis that most process nodes would function similarly during computational modelling of such systems, not depicting the nature of the process whereby one component is transformed to another does impair visual interpretation of the diagrams.
