From Continent to Archipelago: Structural Distortion in Research-to-MOOC Transfer

Wang, Hongren; Bao, Junda; Zhang, Chenwei; Liu, Xiaozhong

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https://hdl.handle.net/2142/132979 Copy

Description

Title

From Continent to Archipelago: Structural Distortion in Research-to-MOOC Transfer

Author(s)

• Wang, Hongren
• Bao, Junda
• Zhang, Chenwei
• Liu, Xiaozhong

Issue Date

2026-03-12

Keyword(s)

• Knowledge structure
• Digital education
• Information literacy

Abstract

Introduction. Massive Open Online Courses (MOOCs) are widely expected to democratize access to frontier science, but this assumes that the structure of knowledge is transferred with fidelity from research to digital education. Method. We compare microelectronics research (2,344 Web of Science (WoS) abstracts, 2020–2024) with microelectronics MOOCs (362 course descriptions, ≤2022). We construct term co-occurrence networks, apply NPMI-based edge pruning (top q=0.50, 0.70, 0.90), and map conceptual geography using non-metric MDS based on Jaccard term–document overlap. Analysis. We compute average path length, modularity, density, and clustering within the largest connected component (LCC). To rule out genre-driven density artifacts, we run density-matched sparsification on the WoS networks (1000 null networks per pruning regime). Results. Across pruning regimes, WoS networks remain denser and more clustered, while MOOCs exhibit longer within-LCC paths. MOOCs also show substantially higher fragmentation (1−LCC) and fall outside the WoS null distributions at all pruning levels (0/1000 simulations). Conclusion(s). Knowledge is reorganized from an integrated “continent” to a fragmented “archipelago” in digital education. This pattern is unlikely to be explained by density differences alone and motivates bridge-building curricula, and AI supports that help learners navigate authentic scientific knowledge structures.

Publisher

iSchools

Series/Report Name or Number

iConference 2026 Proceedings

Type of Resource

Other

Genre of Resource

Conference Poster

Language

eng

Permalink

https://hdl.handle.net/2142/132979

Copyright and License Information

Copyright 2026 is held by Hongren Wang, Junda Bao, Chenwei Zhang, and Xiaozhong Liu. Copyright permissions, when appropriate, must be obtained directly from the authors.

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