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Self-Adaptive Interface for Comprehensive Authoring

Publication Type : Journal Article

Thematic Areas : Amrita e-Learning Research Lab

Publisher : Procedia Computer Science

Source : Procedia Computer Science, Elsevier B.V. (2015)

Url :

Keywords : Adaptive interface, Adaptive user interface, Authoring environments, Authoring process, Computer vision, Curricula, E-learning, Education, Instructional design model, Internet, Massive open online course, Online course, Teaching, User interfaces

Campus : Amritapuri

School : School of Engineering

Center : E-Learning

Department : E-Learning

Year : 2015

Abstract : 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.

Cite this Research Publication : R. Ravi, A. Kumar, Kamal Bijlani, Sharika, T. R., A.P., J., and D., A. - J., “Self-Adaptive Interface for Comprehensive Authoring”, in Procedia Computer Science, 2015.

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