Assessing free student answers in tutorial dialogues using LSTM models


In this paper, we present an LSTM approach to assess free short answers in tutorial dialogue contexts. A major advantage of the proposed method is that it does not require any sort of feature engineering. The method performs on par and even slightly better than existing state-of-the-art methods that rely on expert-engineered features.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)