Prof. Kobi Gal

Modern pedagogical software is open-ended and flexible, allowing students to solve problems through exploration and trial-and-error. Such exploratory settings provide for a rich educational environment for students, but they challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in such pedagogical software and visualizing these activities to teachers. It describes a new plan recognition algorithm that uses a recursive grammar that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using an exploratory environment for teaching chemistry used by thousands of students in several countries. It was always able to correctly infer students' plans when the appropriate grammar was available. We designed two methods for visualizing students' activities for teachers: one that visualizes students' inferred plans, and one that visualizes students' interactions over a timeline. Both of these visualization methods were preferred to and found more helpful than a baseline method which showed a movie of students' interactions. These results demonstrate the benefit of combining novel AI techniques and visualization methods for the purpose of designing collaborative systems that support students in their problem solving and teachers in their understanding of students' performance.