Visualizing knowledge element： a semiotic perspective
Hehai Liu, Hong Rao and Qi Wang
With the development of ubiquitous learning and fragmented learning, visual learning resources are in increasing demand. Learning resources visualization is the key to developing mini resources. Knowledge element is a unit of explicit knowledge and also an example of mini learning resources. Visual representation of knowledge element is the tranormation of mini learning resources from plain text to visual image. Based on information science and cognitive psychology, knowledge is divided into four types： form-focused declarative knowledge element, content-focused declarative knowledge element, utility-focused declarative knowledge element and content-focused procedural knowledge element. A theoretical model is developed and put to use with the purpose of designing visual knowledge elements to reduce learners’ cognitive loads and enhance learning efficiency.
Keywords： knowledge element； knowledge visualization； visual representation； visual design； ubiquitous learning； fragmented learning
Open Learning, Open Networks
Open online learning entered the mainstream with the growth and popularity of MOOCs, but while interest in open online courses has never been greater MOOCs represent only the first step in a broader open learning infrastructure. Adapted from a talk given March 9, 2017, at the State University of New York in Syracuse, this essay describes several key innovations shaping the future of open learning： distributed social networks, cloud infrastructures and virtualization, immersive reality, and personal learning environments. It outlines the challenges this evolving model will pose to learning providers and educational institutions and recommend policies and processes to meet them.
Keywords： MOOC； personal learning； social networks； cloud Infrastructure； virtual reality； artificial intelligence
Correlation analysis of learning behiors and learning style preferences in a self-regulated online learning environment
Juan Yang, Xiaoling Song and Xingmei Qiao
The effectiveness of learning style （LS） theory in an adaptive learning hypermedia （ALH） system remains an open question with controversial voices on its reliability, validity and application effects. This paper reports on a prototype platform designed to collect participants’ learning behior data in a self-regulated online learning environment as well as identify their LS preferences using a LS measurement instrument. Hypotheses are formulated and verified. Experimental results show that the use of LS preferences alone in an ALH system cannot ensure learning effectiveness and that the LS construct should be separated into two parameters： preference and LS features conducive to learning. Effective learning does not occur unless the second parameter is properly leveraged. It is also found that a dynamic LS user model can be more accurate than a static one because it is based on learners’ learning behior data as well as affected by the content they are studying.