Building a specialist agent to assist in the implementation of Game Learning Analytics techniques
Game Learning Analytics (GLA) involves capturing and analyzing data from educational games, enabling the identification of evidence of learning. A fundamental step before implementing GLA techniques is data modeling, which is not trivial. Using large language models (LLMs) can help in this context, as they can generate text like humans. Therefore, considering Chat-GPT and its customizable functionality, “MyGPTs,” this work proposes creating a specialist agent to assist learning designers in data modeling and implementing GLA techniques based on the GLBoard system. Preliminary results with GLA specialists were positive, indicating the agent’s potential.
Authors
- Marcela Pessoa (ORCID: https://orcid.org/0000-0002-3064-5585)
- Fabrizio Honda (ORCID: https://orcid.org/0009-0003-8174-1002)
- Elaine Oliveira (ORCID: https://orcid.org/0000-0003-2884-9359)
- Fernanda Pires (ORCID: https://orcid.org/0000-0002-8180-9850)
Institutions
- Universidade do Estado do Amazonas (BR)
- Universidade Federal do Amazonas (BR)
Publication Details
- Published
- 2024-11-04
- DOI
- https://doi.org/10.5753/sbie.2024.244936
- Citations
- 4
- Primary Topic
- Educational Games and Gamification
- Type
- article
- Field-Weighted Citation Impact
- 3.16
Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa do Estado do Amazonas