Hub keywords in the AIED KCN show disassortative tendencies: popular keywords connect with less popular ones and vice versa¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Most nodes have average weighted nearest neighbor degree above one, while high-degree nodes like 'intelligent tutoring system' connect preferentially with lower-degree nodes. → Shihui Feng & Nancy Law 2021

Evidence¶

Shihui Feng & Nancy Law 2021¶

Shihui Feng & Nancy Law. (2021). Mapping Artificial Intelligence in Education Research: A Network-based Keyword Analysis. International Journal of Artificial Intelligence in Education, 31, 277–303. https://doi.org/10.1007/s40593-021-00244-4

q2 · i? · review · r2

Macro-level analysis of the association between modified average weighted nearest neighbor degree and node degree (Fig. 4). The article finds the majority of nodes have values above one, "indicating a tendency of connecting with nodes that have higher degrees," while hubs connect to lower-degree nodes — a hierarchical feature of the knowledge structure.

"the nodes with a higher degree in the network (e.g. 'intelligent tutoring system') tend to have a value of average weighted nearest neighbor degree below one, suggesting a tendency of connecting with the nodes that have lower degrees."

Discussion¶

Related Claims¶

• The AIED keyword co-occurrence network has a hierarchical structure with a few widely studied hub keywords bridging diverse research topics — a broader claim this one bears on
• AIED research centers on two sustained themes: intelligent tutoring systems (2010–19) and massive open online courses (since 2014) — related