Exploring Social Computing Approaches for Future Education and Convergence Education with Social Algorithm Perspectives

Purpose: This paper discusses convergence education and future education perspectives of computing and algorithms applied to humanistic and social sciences with artificial intelligence. These algorithms are referred to as social algorithms. Methods: This paper considers the social scientific implications of social algorithms from an educational perspective, examining the issues and topics associated with the use of these algorithms for educational practices. Results: This discussion of social algorithms explicitly focuses on examples and case studies of educational practices rather than on theory and techniques. As specific issues for exploring the potential of social algorithms, this paper proposes social algorithm development processes, deductive-inductive approaches to social model building, and geospatial algorithms. It also suggests a more comprehensive list of geospatial data research topics for educational practices associated with social computing, including collective intelligence, community mapping, digital mapping content (i.e. Google Earth), evolutionary algorithms, and digital literacy of geospatial data. Conclusion: Some significant findings are highlighted along with ways that these can be infused with work on social algorithms and computing.