Design of an Adaptive Guidance Mechanism for Next Generation E-learning Based on Knowledge Inference and Platform Collaborations

• Main Authors: Indi W.H. Hu, 胡維桓
• Other Authors: Frank S.C. Tseng
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/qfc4ve

碩士 === 國立高雄第一科技大學 === 資訊管理所 === 96 === Due to the time and space limitation of traditional learning models, e-learning has already been recognized as an effective media for contemporary learners. The rapid growth of the Internet has accelerated the speed of e-learning platform creation and made it popular for interdisciplinary study. However, the current learning platforms still lack of adaptive guidance mechanism for self-learners. In addition, although the interoperability of learning objects has been improved between different platforms, it is insufficient in intersite knowledge sharing, especially for advanced communication between e-learning platforms. In this thesis, we will propose an approach based on the Concept Map, Markov Chain, and Bayesian Network to construct an adaptive guidance system, which provides a self-learning guidance for deriving a so-called Remedial Instruction Decision Path (RIDP) for various learners to accommodate customized learning objectives. The concept of Web 2.0 has featured content reuse, human-centered, social and user participation. That inspires us to propose a Collaboration Game approach to construct a fair cooperating and sharing mechanism for next generation e-learning. Hopefully, our findings could serve as a reference for constructing e-learning platforms of industries, government organizations, and academies with knowledge sharing to step forward to the realm of e-learning 2.0.