Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
This work addresses the need for scalable, personalized education in large-scale tutoring systems, though it appears incremental as it builds on existing machine learning and NLP techniques.
The study tackled the problem of enhancing student learning in intelligent tutoring systems by developing an automated, data-driven approach to generate personalized feedback, resulting in considerable improvements in learning outcomes and subjective feedback evaluations.
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.