A Wiki with Multiagent Tracking, Modeling, and Coalition Formation

Authors

  • Nobel Khandaker University of Nebraska - Lincoln
  • Leen-Kiat Soh University of Nebraska - Lincoln

DOI:

https://doi.org/10.1609/aaai.v24i2.18816

Abstract

Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.

Downloads

Published

2010-07-11

How to Cite

Khandaker, N., & Soh, L.-K. (2010). A Wiki with Multiagent Tracking, Modeling, and Coalition Formation . Proceedings of the AAAI Conference on Artificial Intelligence, 24(2), 1799-1806. https://doi.org/10.1609/aaai.v24i2.18816