Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings

In massive open online courses (MOOCs), recommendation relationships present a collection of associations that imply new form integration, such as an interdisciplinary synergy among diverse disciplines. This study took computer science approach, using the susceptible-infected (SI) model to simulate process learners accessing within networks MOOC offerings, and emphasized potential effects network structure. The current low rate access suggests ceiling effect influences learners’ learning online, given there are thousands freely available. Interdisciplinary were created by adding recommended into four disciplinary networks. diversity interdisciplinarity was measured three attributes, namely variety, balance, disparity. results attest interesting changes in how knowledge grows. Particularly remarkable is degree which increased when first added. However, changing implied neighbouring disciplines more likely come forefront attach pace disparate slowed time passed. absence domain experts, expert not sufficient support curriculum design. More evidence-based analytics studies showing evolves course offerings could help us better design prepare with 21st-century skills.