Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

Authors

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

DOI:

https://doi.org/10.1609/aimag.v36i2.2579

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.

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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

Issue

Section

Articles