The Tuning transdiscplinary education graph below began with keywords and concepts that were extracted from the series of stakeholder interviews conducted as part of IO3. The interview transcripts were subjected to textual analysis using artificial intelligence (AI) machine learning algorithms to uncover underlying patterns and key concepts, resulting in an array of keywords and phrases. The relationships between these keywords and phrases were analysed using network visualisation techniques to explore interconnections, map, and make visible underlying patterns in their network structure and data.

The resulting graph was created using Graph Commons a collaborative platform for mapping, analysing and publishing data-networks that empowers people and organizations to transform their data into interactive maps and untangle complex relations that impact them and their community.

The graph is a work in progress that offers a complementary mode of analysis through a visual representation of the network formed by the intertextual connections underpinning the living practice of transdisciplinary education as communicated by the stakeholders interviewed during the research.

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