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 language | English |
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Title of host publication | LAK 2024 Conference Proceedings - The Fourteenth International Conference on Learning Analytics & Knowledge |
Editors | Srecko Joksimovic, Andrew Zamecnik |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 157-166 |
Number of pages | 10 |
ISBN (Electronic) | 9798400716188 |
DOIs | |
Publication status | Published - 2024 |
Event | International Learning Analytics & Knowledge Conference 2024 - Kyoto, Japan Duration: 18 Mar 2024 → 22 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
Conference | International Learning Analytics & Knowledge Conference 2024 |
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Abbreviated title | LAK 2024 |
Country/Territory | Japan |
City | Kyoto |
Period | 18/03/24 → 22/03/24 |
Internet address |
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Keywords
- Collaborative Problem-Solving
- Epistemic Network Analysis
- Graph Neural Networks
- Social Network Analysis