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A Study on Creating Assessment Model for Miniature Question Answer Using Nearest Neighbor Search Keywords

L.Mary Immaculate Sheela, R.J.Poovaraghan

We introduce a new Assessment model for nearest neighbour search keywords algorithm. The algorithm builds a nearest neighbour model. There are limited types of knowledge that can be assessed by multiple choice tests. In multiple choice questions a student can have simply select a random answer and still have a chance of receiving a mark for it. In a short answer question, the student types in a word or phrase or sentences in response to a question. The answer could be a word or a phrase, but it must match one of our acceptable keywords exactly. It’s a good idea to keep the required answer as short as possible to avoid missing a correct answer that's phrased differently. This feature allows a user to create, preview, and edit questions in a database of question categories. In this paper we are going to compare the entire template model produced by both training and testing phase. Measure the similarities between the two models and identify the quality and efficiency of the result using nearest neighbour keyword. The experimental results show what aspects are important for short answer question type classification in terms of both effectiveness and efficiency. We believe that the assessment model findings from this study will be useful in real-world student problems.