Agent Supported Cooperative Work
Agent Supported Cooperative Work
Agent-based cooperative learning: a proof-of-concept experiment
Proceedings of the 35th SIGCSE technical symposium on Computer science education
Utterance classification in AutoTutor
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
AutoTutor: A simulation of a human tutor
Cognitive Systems Research
A metrics framework for evaluating group formation
Proceedings of the 2007 international ACM conference on Supporting group work
Forming and scaffolding human coalitions with a multi-agent framework
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
International Journal of Artificial Intelligence in Education
Student Learning and Team Formation in a Structured CSCL Environment
Proceedings of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding
Multiagent coalition formation for computer-supported cooperative learning
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Improving Group Selection and Assessment in an Asynchronous Collaborative Writing Application
International Journal of Artificial Intelligence in Education
Proceedings of the 2011 ACM Symposium on Applied Computing
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In this paper, we describe an innovative infrastructure to support student participation and collaboration and help the instructor manage large or distance classrooms using multiagent system intelligence. The system, called I-MINDS, has a host of intelligent agents for each classroom: a teacher agent ranks and categorizes real-time questions from the students and collects statistics on student participation, a number of group agents that each maintains a collaborative group and facilitate student discussions, and a student agent for each student that profiles a student and finds compatible students to form the student's "buddy group". Each agent is capable of machine learning, thus improving its performance and services over time. These agents also interact and collaborate among themselves to exchange information and form coalitions dynamically to better serve the users. We have pilot-tested I-MINDS in GIS lectures, deployed I-MINDS in an introductory computer science course (CS1)'s laboratory, and evaluated the impact of I-MINDS based on student assessment. The results showed that students using I-MINDS performed (and outperformed in some aspects) as well as students in traditional settings.