JavaTutor
Joseph B. Wiggins, Kristy Elizabeth Boyer, Alok Baikadi, Aysu Ezen-Can, Joseph F. Grafsgaard, Eun Young Ha, James C. Lester, Christopher M. Mitchell, Eric Wiebe · 2015
Introductory computer science courses cultivate the next generation of computer scientists. The impressions students take away from these courses are crucial, setting the tone for the rest of the students' computer science education. It is known that students struggle with many concepts central to computer science, struggles that could be alleviated in part through hands-on practice and individualized instruction. However, even the best existing instructional practices do not facilitate individualized hands-on support for students at large. We have built JavaTutor, an intelligent tutoring system for introductory computer science, which works alongside students to support them through both cognitive (skills and knowledge) and affective (emotion-based) feedback. JavaTutor aims to make advances in interactive, scalable student support. JavaTutor's behaviors were developed within a novel framework that leverages machine learning to acquire tutorial strategies from data collected within tutorial sessions between novice students and experienced human tutors. This demo presents an overview of the data-driven development of JavaTutor and shows how JavaTutor assesses and responds to students' contextualized needs. It is hoped that JavaTutor will help to usher in a new generation of tutorial systems for computer science education that adapt to individual students based not only on incoming student knowledge, but on a broad range of other student characteristics.