Abstract
Group formation is the first step and has an important effect on collaborative learning based on CSCL (Computer Support for Collaborative Learning). Heterogeneous groups have been tested to increase interaction in collaborative learning. This research develops intelligent agents based on genetic algorithms to form heterogeneous groups. The level of group heterogeneity is built based on the student's personality traits. Genetic algorithms are used as a method of intelligence to make diversity within a group. Intelligent agent performance is measured based on heterogeneity, and collaboration performance. Intelligent agent testing produces good scores for heterogeneity and collaboration performance. It can be concluded that intelligent agents are successful in forming heterogeneous groups. Heterogeneity based on students is proven to improve the quality of collaborative learning.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
