Plan recognition in virtual laboratories

Ofra Amir, Yakov Gal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students' activities in virtual laboratories can provide important information to teachers as well as serve as the basis for intelligent tutoring. Student use of virtual laboratories presents several challenges: Students may repeat activities indefinitely, interleave between activities, and engage in exploratory behavior using trial-and-error. The plan recognition algorithm uses a recursive grammar that heuristically generates plans on the fly, taking into account chemical reactions and effects to determine students' intended high-level actions. The algorithm was evaluated empirically on data obtained from college students using virtual laboratory software for teaching chemistry. Results show that the algorithm was able to (1) infer the plans used by students to construct their models; (2) recognize such key processes as titration and dilution when they occurred in students' work; (3) identify partial solutions; (4) isolate sequences of actions that were part of a single error.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Second international joint conference on Artificial Intelligence
Place of PublicationBarcelona, Catalonia, Spain
PublisherAAAI Press
Number of pages6
ISBN (Print)978-1-57735-515-1
Publication statusPublished - 16 Jul 2011
EventTwenty-Second International Joint Conference on Artificial Intelligence - Barcelona, Spain
Duration: 16 Jul 201122 Jul 2011


ConferenceTwenty-Second International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI'11
Internet address


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