Self-Adaptive Interface for Comprehensive Authoring

The advent of MOOCs gives, different varieties of teachers with different computer proficiency, the opportunity to create online courses. However, to provide an authoring environment that suits all types of teachers is a challenging task. We address this issue by categorizing the teachers into four broad classes based on their computer proficiency and providing them with customized authoring experience. Our system achieves this by collecting data about the teacher's performance during initial authoring process. This helps the system to identify the category of the teacher and adjust its interface. The system also adheres to ADDIE instructional design model. From the experiments, it is observed that the proposed system showed a better performance among teachers than the existing authoring environment.