Research On Aggregation And Sharing Of Open Learning Resources Based On Collective Intelligence

The study in Web3.0era is in the digital network environment of “rich tool” and“rich resource”. The digital learning resources are rich day by day, access channels areincreasingly diverse, the learning concept is changed continuously, learning method isinnovated constantly, knowledge is updated faster and faster, thus individual cannotmaster all the knowledge. It is important to seek the source of knowledge andinter-related relationship of knowledge than learning knowledge itself when eachindividual needs these resources. Meanwhile, the rich open learning resources provideequal opportunities for learners, while learners get lost in the resources when theyenjoy rich resources and they cannot really find the resources they need. How toaggregate the needed resources, build personal knowledge network, then share theopen learning resources to use them efficiently has become hot research topic amongscholars home and abroad.Combining the concept of “learning—linking of knowledge”, this study startedfrom Connectivism and emphasized the individual active participation. Thatcomprehensively propelled the research on aggregation and sharing of open learningresources. The methods used in this study are the method of “co-build”,“collaborativeediting”,“co-evaluation” and “folksonomy”. This study designed the framework ofopen learning resource aggregation and sharing, which based on collectiveintelligence, the research focused on two core components of the framework, two corecontents and three running safeguard mechanisms. According to the research results，the learning platform is designed and conducted to verify its effectiveness. The studyincludes the following four parts:(1) In the study of two core components of the “learner model” and “domainknowledge model”, learner model is built on the learner’s behavior. It is the basis ofaggregation and sharing of open learning resources and occupies a very importantposition in the research. Individual knowledge model form the basis of the domainknowledge model, which functions as a media, connecting knowledge with openlearning resources in the whole study.(2) In the research of two core contents of the “aggregation open learningresources “and “sharing open learning resources”, domain knowledge model isconstructed on the basis of learner model, and is used the methods of “co-build”, “collaborative editing”,“co-evaluation”，and “folksonomy”. Open learning resourcesare aggregated through individual and collective way or through manual andautomatic approach. Then these open learning resources are shared through activelearners and system recommendation(3) Maslow’s hierarchy of needs is used to design incentive mechanism oflearner participation in the research of three guarantee mechanisms, which are“incentive mechanism of learner participation”,“protection mechanism of openlearning resources” and “visualization mechanism”. The protection mechanism ofopen learning resource is designed on the growth, evolution of resources, as well asother ecological rules. Tag clouds and knowledge map are used to design visualizationmechanism, which protect the efficient operation of aggregation and sharing of openlearning resources based on collective intelligence.(4) The framework of the platform and the related object model, and therelational model are designed. The platform of resources aggregation and sharingbased on collective intelligence is implemented. The study result is evaluated in theperspective of “subjective” and “objective”,“qualitative” and “quantitative”,“learner” and “resource”. The findings achieve good results in learner’s participation,satisfaction, aspiration，resources aggregation and sharing effect.In this study, two innovations are realized, which are “self-learning concept” and“aggregation and sharing methods of open learning resource”. Firstly, with theconcept of “learning—linking of knowledge” running through the study, the processof learning is integrated into the construction of social knowledge network. The studyemphasized the active participation of learners, transforming pure knowledge learninginto knowledge acquisition experience, which gives full play to the “collectiveintelligence” through the interaction of learners. Secondly, the framework ofaggregation and sharing of open learning resources is broken. The research focuses onthe way of aggregation and sharing of open learning resources instead of meretechnical terms. The subject shifts from system to learners. Learners’ activeparticipations are respected with the application of different research methods, like“co-build”,“collaborative editing”,“co-evaluation” and “folksonomy”, which givefull play to “collective intelligence”. This study relies on the semantic comprehensionand processing ability of individuals instead of using computer algorithms to calculatethe semantic meaning of language, which will intervene on the results of theaggregation and sharing of open learning resources. The study tries to makeexploratory innovation to achieve the aggregation and sharing of open learningresources.