Research On Blockchain-based Online Learning Incentive Model

With the development of information technology,the online learning platform represented by MOOC has gradually changed the way people acquire knowledge.Teaching and learning are no longer restricted by the conditions of time,space and location,the channels of knowledge acquisition are more flexible and diverse.However,due to the lack of an effective incentive mechanism,online learning platforms face problems of low knowledge sharing efficiency and low long-term user participation.Existing online learning platforms generally complete incentives through the issuance of digital completion certificates and rewards for learning points,but the centralized data storage method makes cross-platform verification impossible for certificates and points,which makes it difficult to guarantee the validity..In response to the above problems,this paper uses the characteristics of decentralization and anti-tampering of the blockchain,combining the blockchain with online learning to design and implement an incentive mechanism model for online learning.The model consists of two parts: digital certificate completion incentive and online learning platform contribution points incentive.Through these two incentive methods,it can not only motivate students to complete online teaching content,but also motivate creators of teaching content to release high-quality teaching content,which improves online teaching quality.The main work of this paper is as follows:First,the digital completion certificate issuance module in the online learning incentive model was designed,and an education alliance chain was established with the participation of online learning platforms,universities,and enterprises.Certificate management is the core business of the alliance chain and the certificates are issued and verified through chaincode(smart contracts).The issuance of the certificate is endorsed by all the participants in the education alliance chain,which guarantees the authenticity and validity.This module is a coarse-grained incentive for users of online learning platforms and can enhance students' online learning motivation.Second,a contribution point issuance module in the online learning incentive model is designed.This module issues point rewards that represent platform contribution values through smart contracts.The distribution of contribution points requires the endorsement of all participants in the education alliance chain,which ensures the social recognition and authenticity.In this model,content creators and learners receive points rewards through knowledge sharing.In this module,the contribution point issuance model is simulated to verify the feasibility.The contribution point distribution model is a fine-grained incentive for users of online learning platforms,thereby improving the quality of teaching content on the platform and promoting the healthy development of online learning platforms.Third,an improved consensus algorithm HPBFT is proposed to meet the performance requirements of the online learning incentive model.The algorithm introduces a node integration mechanism and selects nodes participating in consensus based on the node integration to reduce the communication overhead in the network.In the absence of Byzantine nodes,we optimize the consistency protocol of the PBFT algorithm,and introduce an up-down mechanism on the basis of the node integration mechanism to dynamically update the set of nodes participating in the consensus to ensure that the optimized consistency protocol is executed most of the time.