A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

Abstract

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a wide range of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 and has over 7, 000 students have enrolled. 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.

Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching

“…Thus, the essential role of ITSs for mathematics learning has been acknowledged, and the use of ITSs to help students learn mathematics has become an important research topic (AbuEloun & Naser, 2017;Hsieh & Chen, 2019;Mokmin, 2020;Paiva et al, 2017). Referring to dialogue-based math ITSs, Kochmar et al (2020) implemented machine learning and natural language-processing technologies in a large-scale dialoguebased ITS, named Korbit (Serban et al, 2020), and they verified the effectiveness of improving students' learning outcomes. In the teaching program, they used a natural language-processing technique to compare students' solutions against stored expected solutions.…”

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

“…Many ITSs provide feedback guiding students towards the correct solution in an exercise (Ramachandran et al 2018;Rus, Niraula, and Banjade 2015;Nye, Graesser, and Hu 2014;Ventura et al 2018;Al-Nakhal and Abu-Naser 2017;Al-Dahdooh and Abu-Naser 2017;Tamura et al 2015;Guo et al 2016;Shah, Shah, and Kurup 2017;Serban et al 2020;Kochmar et al 2020). However, to our knowledge, none have previously employed a neural discourse-based mechanism for personalized feedback.…”

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

“…Many ITSs provide feedback guiding students towards the correct solution in an exercise (Ramachandran et al 2018;Rus, Niraula, and Banjade 2015;Nye, Graesser, and Hu 2014;Ventura et al 2018;Al-Nakhal and Abu-Naser 2017;Al-Dahdooh and Abu-Naser 2017;Tamura et al 2015;Guo et al 2016;Shah, Shah, and Kurup 2017;Serban et al 2020;Kochmar et al 2020). However, to our knowledge, none have previously employed a neural discourse-based mechanism for personalized feedback.…”