Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

  • Oriel Uzan Ben-Gurion University
  • Reuth Dekel Ben-Gurion University
  • Or Seri Ben-Gurion University
  • Ya’akov (Kobi) Gal Ben-Gurion University.

Abstract

This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.
Published
2015-06-21
How to Cite
Uzan, O., Dekel, R., Seri, O., & Gal, Y. (Kobi). (2015). Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search. AI Magazine, 36(2), 10-21. https://doi.org/10.1609/aimag.v36i2.2579
Section
Articles