OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research

要旨

Despite decades long establishment of effective tutoring principles, no adaptive tutoring system has been developed and open-sourced to the research community. The absence of such a system inhibits researchers from replicating adaptive learning studies and extending and experimenting with various tutoring system design directions. For this reason, adaptive learning research is primarily conducted on a small number of proprietary platforms. In this work, we aim to democratize adaptive learning research with the introduction of the first open-source adaptive tutoring system based on Intelligent Tutoring System principles. The system, we call Open Adaptive Tutor (OATutor), has been iteratively developed over three years with field trials in classrooms drawing feedback from students, teachers, and researchers. The MIT-licensed source code includes three creative commons (CC BY) textbooks worth of algebra problems, with tutoring supports authored by the OATutor project. Knowledge Tracing, an A/B testing framework, and LTI support are included.

著者
Zachary A.. Pardos
UC Berkeley, Berkeley, California, United States
Matthew Tang
University of California Berkeley, Berkeley, California, United States
Ioannis Anastasopoulos
University of California Berkeley, Berkeley, California, United States
Shreya K.. Sheel
University of California, Berkeley, Berkeley, California, United States
Ethan Zhang
University of California, Berkeley, Berkeley, California, United States
論文URL

https://doi.org/10.1145/3544548.3581574

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Interactive Learning Support Systems

Hall G1
6 件の発表
2023-04-26 23:30:00
2023-04-27 00:55:00