Identifying Important Knowledge Through Node-Level Concept Network Analysis: Addressing “What to Teach”
Year: 2025
Type: article
Abstract: Not all knowledge is equally important in teaching and learning, yet identifying important knowledge remains a fundamental challenge in curriculum design. Traditional methods rely on expert judgment and lack systematic, reproducible criteria. This study demonstrates that node-level concept network analysis can provide operational definitions for important knowledge by deconstructing their characteristics into quantifiable structural features. Using a mathematics concept network, we analyzed both Big Ideas and International Baccalaureate (IB) key concepts, examining whether their characteristics can be systematically deconstructed into network indicators. The analysis successfully deconstructs important knowledge characteristics into structural features, with the deconstruction process demonstrating stability across independent optimization algorithms. While specific indicators (such as degree centrality) emerge from the analysis, the fundamental contribution is establishing the analytical framework itself. This research illustrates the potential of concept network analysis for addressing fundamental challenges in curriculum design and knowledge-focused educational research.
Source: Systems
Authors: Xiang Cao, Jinshan Wu
Institution: Beijing Normal University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416945487