Both problem-oriented learning and case-based learning are effective methods for practical knowledge development. However, an automatic development of learning cases for adaptive learning is still an open issue. To support adaptive case-based learning in a proposed problem-oriented e-learning (POeL) environment and to address the complexity and diversity of the learning problems of students with mild disabilities, this study presents a learning case adaptation framework to support problem-oriented e-learning. This framework provides mechanisms to search and match similar learning cases according to encountered teaching problems by information retrieval techniques and to develop an adaptive learning case by adaptation techniques. Adaptation techniques include a substitution technique, a removal technique, and a composition technique, and utilize cosine-measure and genetic algorithm. In this research, adaptive learning cases were developed for teaching students with mild disabilities so as to assist regular and special education teachers to develop practical knowledge of teaching more effectively.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence