Assessing free student answers in tutorial dialogues using LSTM models
Abstract
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)
Recommended Citation
Maharjan, N., Gautam, D., & Rus, V. (2018). Assessing free student answers in tutorial dialogues using LSTM models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10948 LNAI, 193-198. https://doi.org/10.1007/978-3-319-93846-2_35
