Adaptive natural-language targeting for student feedback

Abstract

In tutoring software, targeting feedback to students' natural-language inputs is a promising avenue for making the software more effective. As a case study, we built such a system using Natural Language Processing (NLP) to provide adaptive feedback to students in an online learning task. We found that the NLP targeting mechanism, relative to more traditional multiple-choice targeting, was able to provide optimal feedback from fewer student interactions and generalize to previously unseen prompts.