%0 Conference Proceedings %T Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? %A Zeng, Zijie %A Li, Xinyu %A Gasevic, Dragan %A Chen, Guanliang %Y Carpuat, Marine %Y de Marneffe, Marie-Catherine %Y Meza Ruiz, Ivan Vladimir %S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2022 %8 July %I Association for Computational Linguistics %C Seattle, United States %F zeng-etal-2022-deep %X Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education. However, these techniques often suffer from their inabilities to explain and justify how a prediction is made, which, unavoidably, decreases their trustworthiness and hinders educators from embracing them in practice. This study aimed to investigate whether (and to what extent) DL-based graders align with human graders regarding the important words they identify when marking short answer questions. To this end, we first conducted a user study to ask human graders to manually annotate important words in assessing answer quality and then measured the overlap between these human-annotated words and those identified by DL-based graders (i.e., those receiving large attention weights). Furthermore, we ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL-based graders on human grading. The results showed that: (i) DL-based graders, to a certain degree, displayed alignment with human graders no matter whether DL-based graders and human graders agreed on the quality of an answer; and (ii) it is possible to facilitate human grading by highlighting those DL-detected important words, though further investigations are necessary to understand how human graders exploit such highlighted words. %R 10.18653/v1/2022.naacl-main.14 %U https://aclanthology.org/2022.naacl-main.14 %U https://doi.org/10.18653/v1/2022.naacl-main.14 %P 191-205