Recognizing opportunities for mixed-initiative interactions based on the principles of self-regulated learning

Jurika Shakya, Samir Menon, Liam Doherty, Mayo Jordanov, Vive Kumar

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

2 Citations (Scopus)

Abstract

Successful mixed initiative systems employ mechanisms that explicitly recognize opportunities for initiatives among the system and the users. In this paper, we propose a theory based framework, founded on the principles of Self Regulated Learning, that recognizes strategies and tactics learners used in their interactions. These interactions are observed from within gStudy, a learning tool that students use as part of learning activity. Production rules encode SRL-specific knowledge in an OWL-based formal ontology and JESS is used as an inference engine to recognize strategies and tactics used by learners in specific reading and problem-solving activities. Using such inferences we demonstrate how the system recognizes opportunities for mixed-initiative interactions to guide learners who veer away from optimal SRL strategies.

Original languageEnglish
Title of host publicationAAAI Fall Symposium - Technical Report
Pages117-122
Number of pages6
Publication statusPublished - 2005
Event2005 AAAI Fall Symposium - Arlington, VA, United States
Duration: 4 Nov. 20056 Nov. 2005

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-05-07

Conference

Conference2005 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA
Period4/11/056/11/05

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