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Title: Analyzing saliency in neural models for scoring content in science explanations
Models for automated scoring of content in educational applications continue to demonstrate improvements in human-machine agreement, but it remains to be demonstrated that the models achieve gains for the “right” reasons. For providing reliable scoring and feedback, both high accuracy and connecting scoring decisions to scoring rubrics are crucial. We provide a quantitative and qualitative analysis of automated scoring models for science explanations of middle school students in an online learning environment that leverages saliency maps to explore the reasons for individual model score predictions. Our analysis reveals that top-performing models can arrive at the same predictions for very different reasons, and that current model architectures have difficulty detecting ideas in student responses beyond keywords.  more » « less
Award ID(s):
1812660
PAR ID:
10286236
Author(s) / Creator(s):
Date Published:
Journal Name:
BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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