Blockchain-based governance models supporting corruption-transparency:A systematic literature review

Corruption and lack of transparency remain critical challenges in governance systems around the world.These issues are often perpetuated by centralized systems and their manipulation by system administrators.Furthermore,the lack of data ownership and the monetization of user data by tech companies further increase concerns about transparency.In light of these concerns,this study aims to review existing blockchain-based governance models and identify best-practice governance models focusing on corruption transparency,their characteristics,and components.The research will also examine the role of a token economy in addressing trusted third-party issues related to asset ownership management.Furthermore,we discuss the effect of smart contracts,blockchain,decentralized autonomous organizations(DAOs),Web 3.0,and multifactor challenge set self-sovereign identity authentication(MFSSIA)as modern technologies to combat corruption and achieve transparency in the public sector.To achieve this,we conducted a systematic literature review(SLR)comprising peer-reviewed journals,proceedings,and book chapters published between 2012 and 2023.Using the SLR methodology,45 primary and supporting studies have been selected for result extraction and analysis.Finally,we discovered seven blockchain-based governance models with their characteristics and primary components.

Toward a data analytics -based approach for computing and predicting context- sensitive teaching effectiveness

The traditional student-oriented course evaluation has been the major assessment method on teaching effectiveness worldwide.Useful as it is,it has been widely and continuously criticized for not being a fair,accurate,and reliable measurement.In search of a more objective assessment method on teaching effectiveness that also reflects the impacts of context-based learning,we propose a theoretical approach from a unique perspective that recognizes teaching effectiveness as a result of the interplays between teacher,student,and context.The approach can be used to compute as well as to predict teaching effectiveness using machine and deep learning technologies,which brings strategical benefits to institutional management.In addition,we install into the approach a mechanism using tokens as incentives to assure the quality of subjective data input.The application framework for the approach is proposed leveraging blockchain.Each implementation of the framework by an establishment is a decentralized application that runs on its chosen blockchain.It is envisioned that the implementations together will form a collective ecology on context-based relative teaching effectiveness,which has the potential to fundamentally impact other academic practices besides teaching effectiveness measurement.The theoretical approach provides a common language to delineate teaching effectiveness from the context-based relative perspective and is customizable during implementation.The teaching effectiveness assessment using the approach downplays the roles played by bias(subjectity)and hence is more objective than that by traditional student-oriented course evaluation.