Neural epistemic network analysis: combining graph neural networks and epistemic network analysis to model collaborative processes

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Abstract

We report on the design and evaluation of a novel technique for analysing the sociocognitive nature of collaborative problem-solving - neural epistemic network analysis (NENA). NENA combines the computational power and representational ability of graph neural networks (GNNs) to naturally incorporate social and cognitive features in the analysis with the interpretative advantages of epistemic network analysis (ENA). Comparing NENA and ENA on two datasets from collaborative problem-solving contexts, we found that NENA improves upon ENA's ability to distinguish between known subgroups in CPS data, while also improving the interpretability and explainability of GNN results.

Original languageEnglish
Title of host publicationLAK 2024 Conference Proceedings - The Fourteenth International Conference on Learning Analytics & Knowledge
EditorsSrecko Joksimovic, Andrew Zamecnik
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages157-166
Number of pages10
ISBN (Electronic)9798400716188
DOIs
Publication statusPublished - 2024
EventInternational Learning Analytics & Knowledge Conference 2024 - Kyoto, Japan
Duration: 18 Mar 202422 Mar 2024
Conference number: 14th
https://dl.acm.org/doi/proceedings/10.1145/3636555 (Conference Proceedings)
https://www.solaresearch.org/events/lak/lak24/
https://ceur-ws.org/Vol-3667/ (LAK 2024 Workshop Proceedings)

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2024
Abbreviated titleLAK 2024
Country/TerritoryJapan
CityKyoto
Period18/03/2422/03/24
Internet address

Keywords

  • Collaborative Problem-Solving
  • Epistemic Network Analysis
  • Graph Neural Networks
  • Social Network Analysis

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