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Developments in the educational landscape have spurred greater interest inthe problem of automatically scoring short answer questions. A recent sharedtask on this topic revealed a fundamental divide in the modeling approachesthat have been applied to this problem, with the best-performing systems splitbetween those that employ a knowledge engineering approach and those thatalmost solely leverage lexical information (as opposed to higher-levelsyntactic information) in assigning a score to a given response. This paperaims to introduce the NLP community to the largest corpus currently availablefor short-answer scoring, provide an overview of methods used in the sharedtask using this data, and explore the extent to which moresyntactically-informed features can contribute to the short answer scoring taskin a way that avoids the question-specific manual effort of the knowledgeengineering approach.

Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring