The automated scoring or evaluation for written student responses have been, and are still a highly interesting udtopic for both education and natural language processing, NLP, researchers alike. With the obvious motivation of udthe difficulties teachers face when marking or correcting open essay questions; the development of automatic udscoring methods have recently received much attention. In this paper, we developed and compared number udof NLP techniques that accomplish this task. The baseline for this study is based on a vector space model, udVSM. Where after normalisation, the baseline-system represents each essay by a vector, and subsequently udcalculates its score using the cosine similarity between it and the vector of the model answer.udThis baseline is then compared with the improved model, which takes the document structure into account. udTo evaluate our system, we used real essays that submitted for computer science course. Each essay was udindependently scored by two teachers, which we used as our gold standard. The systems’ scoring was then udcompared to both teachers. A high emphasis was added to the evaluation when the two human assessors are udin agreement. The systems’ results show a high and promising performance.
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