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Comparative Study of Feature Engineering for Automated Short Answer Grading

Recent advancement in the field of Natural Language Processing (NLP) has shown promising results in numerous NLP applications. Automated Short Answer Grading (ASAG) is one of the challenging applications of NLP. Understanding the underlying context and semantics in text will help to evaluate short answer responses and predict the scores more similar to human evaluator. Training machine to understand the domain knowledge of courses and utilize the same for evaluation has demanded for more robust method to generate word vectors which are capable of understanding the meaning of words based on context. In this paper we have studied and experimented with already existing word vector generation techniques TF-IDF, word2vec and BERT. Along with the pre-trained word2vec we have computed domain specific word vectors too. Finally, by the utilization of XGBoost we have computed the category of student answer. The comparison of similarity between human evaluated category and category generated by all studied embedding technique along with XGBoost algorithm is computed with the help of Quadratic Weighted Kappa. The results have shown that domain specific embedding will help to predict correct category of student answer.