Estimating Similarity for Grading Descriptive Handwritten Answers

Abstract Evaluating descriptive answers is complex and challenging due to the multiple answers provided by students. Although there are approaches for solving the problem of descriptive answer grading, the focus of such methods is on short answers rather than long answers. This work proposes a new model based on estimating similarity scores between question-answers and schema and answers. The proposed work asserts that the schema provides the right words and sentences, considered references for grading descriptive answers. Similarly, the question words also provide vital information about the right answers. We explore the SentenceTransformer model and cosine distance measure to estimate the similarity score, which results in feature vectors. The SentenceTransformer is proposed for estimating semantic similarity between two sentences, while cosine similarity is proposed for estimating the degree of similarity between two words. Inspired by the success of the 1D convolutional neural network in classification, we adopted the 1D-CNN with fully connected dense layers for grading answers by feeding the feature vectors. This CNN model classifies descriptive answers into three categories: A (right answer), B (partially correct answer), and C (wrong answer). To demonstrate the significance of the proposed method, we conducted experiments on our dataset and compared its performance with state-of-the-art methods. The results show that the proposed model outperforms the existing methods in terms of the confusion matrix and average classification rate.