We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
@article{arxiv.2005.02431,
title = {Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System},
author = {Ekaterina Kochmar and Dung Do Vu and Robert Belfer and Varun Gupta and Iulian Vlad Serban and Joelle Pineau},
journal= {arXiv preprint arXiv:2005.02431},
year = {2020}
}
Comments
To be published in Proceedings of the the 21st International Conference on Artificial Intelligence in Education (AIED 2020)