With the explosion of knowledge and information in the big data era, learning new things efficiently is of crucial significance. Despite recent development of e-learning techniques which have broken the temporal and spatial barriers for learners, it is still very difficult to meet the requirement of efficient learning, as the key issues involve not only searching for learning resources but also identification of learning paths. People from diverse backgrounds, in most cases, also need to work as a group to acquire new knowledge or skills and complete certain tasks. As these tasks are normally assigned with time constraints, employment of e-learning systems may be the optimal approach. In this research, we study the issue of identifying a suitable learning path for a group of learners rather than a single learner in an e-learning environment. Particularly, a profile-based framework for the discovery of group learning paths is proposed by taking various learning-related factors into consideration. We also conduct experiments on real learners to validate the effectiveness of the proposed approach. Copyright © 2017 Elsevier B.V.
|Early online date||Mar 2017|
|Publication status||Published - Sept 2017|
CitationXie, H., Zou, D., Wang, F. L., Wong, T.-L., Rao, Y., & Wang, S. H. (2017). Discover learning path for group users: A profile-based approach. Neurocomputing, 254, 59-70. doi: 10.1016/j.neucom.2016.08.133
- Group modeling
- User profile
- Learning path
- Collaborative learning