Literatures have indicated that well-balanced groups facilitate students’ learning performance in collaborative learning environments. For instructors, to construct well-balanced groups needs to take efforts and time to consider large number of students and characteristics. Hence, how to automatically construct well-balanced collaborative learning groups has been a popular issue for collaborative learning. This paper proposes a genetic algorithm (GA)- based grouping strategy to assist instructors in constructing inter-homogeneous and intra-heterogeneous collaborative learning groups considering multiple student characteristics. Several data sets with different problem sizes, such as number of students and characteristics, are employed as experimental materials. Experimental results have demonstrated that the proposed grouping method is effective, efficient and robust.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||12th International Conference on Web-Based Learning, ICWL 2013, held with 1st International Workshop on Ubiquitous Social Learning, USL 2013, International Workshop on Smart Living and Learning, IWSLL 2013, International Workshop on Cloud Computing for Web-Based Learning, IWCWL 2013, International Workshop on Web Intelligence and Learning, WIL 2013, International Workshop on E-book and Education Cloud, IWEEC 2013 and 3rd International Symposium on Knowledge Management and E-Learning, KMEL 2013|
|Period||13-10-06 → 13-10-09|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)