英文摘要

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 transformation 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

Stephen Downes

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 behaviors 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 behavior 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 behavior data as well as affected by the content they are studying.endprint

Keyword： learning style； adaptive hypermedia learning system； learning behavior； learning style preference； Silverman & Felder model； field dependent/independent model； VARK model

An analysis of factors affecting MOOC learners' continuous learning： a review of CNKIs empirical research （2011-2016）

Tianlinzi Sun and Shusheng Shen

Low completion rate of MOOCs has continued to be a predicament faced by MOOC developers and identifying its causes is the key to ensuring the sustainable development of MOOCs. This paper reviews relevant empirical literature published between 2011 and 2016 and stored in the database ‘China National Knowledge Infrastructure （CNKI） in an attempt to find out factors affecting Chinese MOOC learners continuous learning. Two causes are identified. One is related to learners internal factors， including learning motivation， individual experience and perceptual ability. The other is externally related， i.e. factors in relation to teaching and management， including course design， platform management and accreditation system. Implications of these findings are also discussed for the sustainable development of MOOCs in China.

Keywords： MOOC； continuous learning； influencing factor； learning effect； empirical study； literature review

The composition of deep learning content and its reshaping strategies

Hang Hu and Yuqi Dong

This paper reviews learning content in current basic education， drawing on research in deep learning and implementation frameworks. Informed by the ecological curriculum theory and the practice and balanced model， it analyses the composition and features of learning content， develops a deep learning content framework featuring subject knowledge， strategies to knowledge， social skills and cognitive structure as well as establishes an ‘ecological flow curriculum operation paradigm. A research and implementation framework for reshaping deep learning content is also formulated which highlights the roles of individual teachers， community of teachers and teaching community. Reshaping strategies are also proposed to facilitate the selection， organization and implementation of classroom-based deep learning content.

Keywords： deep learning； ecological curriculum theory； learning content； composition； reshaping； implementation studyendprint