Robert Belfer

TL;DR: The authors proposed a machine learning approach to generate personalized feedback, which takes individual needs of students into account, and demonstrated that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.

TL;DR: Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.

TL;DR: This work proposes a machine learning approach to generate personalized feedback, which takes individual needs of students into account, and utilizes state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints.

TL;DR: This paper proposes a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules.

TL;DR: In this paper, the authors explore creating automated, personalized feedback in an intelligent tutoring system (ITS) by decomposing student answers using neural discourse segmentation and classification techniques, yielding a relational graph over all discourse units covered by the reference solutions and student answers.