These shortcomings were addressed by the creation of a diagrammatic language definition language, the introduction of a new component for feedback summarization and a redesign of the diagram editor. The first attempt to integrate these features to support diagram assessment revealed a number of shortcomings, such as the lack of support for multiple diagrammatic languages, ineffective feedback, and usability issues. This form of assessment is supported by a set of new features, including a diagram editor, a graph comparator, and an environment for integration of pedagogical activities. The current version of Mooshak supports other forms of assessment used in computer science, such as diagram assessment. It was originally developed for programming contest management but evolved to be used also as a pedagogical tool, capitalizing on its programming assessment features. Mooshak is a web system with support for assessment in computer science. Several experiments were designed to analyse the impact of different features such as graph size, and amount of difference between solution and attempt. The proposed algorithm was validated with thousands of graphs with different features produced by a synthetic data generator. However, the motivation for developing this algorithm is to combine it with other assessment models, such as the test case model used for programming language assessment. This data model is able to accommodate diagram languages, such as UML or ER diagrams, for which this kind of assessment is typically used. The proposed algorithm is applicable to any type of document that can be parsed into its graph-inspired data model. The proposed algorithm uses heuristics to test the most promising mappings first and prune the remaining when it is sure that a better mapping cannot be computed. Given two graphs, a solution and an attempt of a student, this approach computes a mapping between the node sets of both graphs that maximizes the student’s grade, as well as a description of the differences between the two graph. This paper proposes a structure driven approach to assess graph-based exercises.
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