Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students
Noboru Matsuda, Victoria Keiser, Rohan Raizada, Arthur Tu, Gabriel Stylianides, William W. Cohen et al.
The purpose of the current study is to test whether we could create a system where students can learn by teaching a live machine-learning agent, called SimStudent. SimStudent is a computer agent that interactively learns cognitive skills through its own tutored-problem solving experience. We have developed a game-like learning environment where students learn algebra equations by tutoring SimStudent. While Simulated Students, Teachable Agents and Learning Companion systems have been created, our study is unique that it genuinely learns skills from student input. This paper describes the overview of the learning environment and some results from an evaluation study. The study showed that after tutoring SimStudent, the students improved their performance on equation solving. The number of correct answers on the error detection items was also significantly improved. On average students spent 70.0 minutes on tutoring SimStudent and used an average of 15 problems for tutoring.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-13388-6_36.