Machine Learning in Conversational Analysis to Recommend Collaboration in Discussion Forums
Abstract
The Conversational Analysis (CA) allows the diagnosis of the collaboration level among students in discussion forums of Virtual Learning Environments (VLEs), resulting in indices that make it possible to assess collaboration and, based on this assessment, perform the recommendations to promote collaborative learning continuously. Having gathered a dataset of these indices, it is possible to use Machine Learning (ML) to cluster students and then specialize the recommendations considering their similarities. In this paper, the application of unsupervised ML in obtained indices from the CA is proposed in order to refine and validate a recommendation strategy which promotes the collaboration in discussion forums of VLEs.
Keywords:
Conversational Analysis, ComputerSupported Collaborative Learning, Collaborative Learning Assessment, Machine Learning, Artificial Intelligence in Education
References
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Ahmadian Yazdi, H., Seyyed Mahdavi Chabok, S. J. and Kheirabadi, M. (2022). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence, v. 36, n. 1, p. 2005298.
Anaya, A. R., Luque, M. and Peinado, M. (1 mar 2016). A visual recommender tool in a collaborative learning experience. Expert Systems with Applications, v. 45, p. 248–259.
Farrow, E., Moore, J. and Gašević, D. (2019). Analysing discussion forum data: a replication study avoiding data contamination. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19. Association for Computing Machinery.
Gašević, D., Joksimović, S., Eagan, B. R. and Shaffer, D. W. (2019). SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, v. 92, p. 562–577.
Koschmann, T. (2013). Conversation Analysis and Collaborative Learning. In: Hmelo-Silver, C.; Chinn, C.; Chan, C.; O’Donnell, A.[Eds.]. The International Handbook of Collaborative Learning. New York: Routledge Handbooks. p. 149–167.
Liu, Q., Huang, J., Wu, L., Zhu, K. and Ba, S. (2019). CBET: design and evaluation of a domain-specific chatbot for mobile learning. Universal Access in the Information Society.
Michaels, S. (2016). Accountable Talk® Sourcebook: For Classroom Conversation that Works. p. 46.
Michaels, S. and O’Connor, C. (2015). Conceptualizing Talk Moves as Tools: Professional Development Approaches for Academically Productive Discussions. In: Resnick, L. B.; Asterhan, C. S. C.; Clarke, S. N. [Eds.]. Socializing Intelligence Through Academic Talk and Dialogue. American Educational Research Association. p. 347–361.
Monteverde, I., Amaral, G., Ramos, D., et al. (2017). M-Cluster: Uma ferramenta de Recomendação para Formação de Grupos em Ambientes Virtuais de Aprendizagem. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), v. 28, n. 1, p. 1657.
Moraes Neto, A. J., Costa, N. T., Fernandes, M. A. and Amiel, T. (2022). Análise Conversacional para Diagnosticar e Recomendar a Colaboração em Ambientes Virtuais de Aprendizagem. In Anais do Simpósio Brasileiro de Informática na Educação. SBC. [link], [accessed on Dec 3].
Moraes Neto, A. J., Fernandes, M. A. and Amiel, T. (2020). Chatbot e Análise Conversacional para Recomendação da Aprendizagem Colaborativa na EaD. Anais do Simpósio Brasileiro de Informática na Educação, p. 1142–1151.
Moraes Neto, A. J., Fernandes, M. A. and Amiel, T. (2022). Conversational Analysis to Recommend Collaborative Learning in Distance Education. [link], [accessed on Aug 12].
Oliveira, I., Tinoca, L. and Pereira, A. (2011). Online group work patterns: How to promote a successful collaboration. Computers & Education, v. 57, n. 1, p. 1348–1357.
Pereira, A. J. (2021). Especificação de sistema de recomendação a partir de análise de interações em Rede Social Educacional. Master Thesis.
Pereira, A. J., Gomes, A. S. and Primo, T. T. (2022). Design de Sistema de Recomendação Educacional: abordagens com Mágico de Oz. In Anais do Simpósio Brasileiro de Informática na Educação. SBC. [link], [accessed on Jan 5].
Procter, M., Lin, F. and Heller, B. (2018). Intelligent intervention by conversational agent through chatlog analysis. Smart Learning Environments, v. 5, n. 1, p. 30.
Recuero, R. (2012). A Conversação como Apropriação na Comunicação Mediada pelo Computador. In: Buitoni, D. S.; Chiachiri, R. [Eds.]. Comunicação, Cultura de Rede e Jornalismo. 1a ed. São Paulo: Almedina. p. 259–274.
Stahl, G. (2012). A view of Computer-Supported Collaborative Learning research and its Lessons for Future-Generation Collaboration Systems.
Tegos, S., Demetriadis, S., Psathas, G. and Tsiatsos, T. (2020). A Configurable Agent to Advance Peers’ Productive Dialogue in MOOCs. [A. Følstad, T. Araujo, S. Papadopoulos, et al., Eds.] In Chatbot Research and Design. Lecture Notes in Computer Science. Springer International Publishing.
Uthus, D. C. and Aha, D. W. (2013). Multiparticipant chat analysis: A survey. Artificial Intelligence, v. 199–200, p. 106–121.
Vera, A. A. and González, C. M. (2022). Educational Resource Recommender Systems Using Python and Moodle. [O. Gervasi, B. Murgante, S. Misra, A. M. A. C. Rocha, & C. Garau, Eds.]In Computational Science and Its Applications – ICCSA 2022 Workshops. Lecture Notes in Computer Science. Springer International Publishing.
Zawacki-Richter, O., Marín, V. I., Bond, M. and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, v. 16, n. 1, p. 39.
Published
2024-11-04
How to Cite
MORAES NETO, Antônio J.; VASCONCELOS, Raimundo C. S.; LIMA, Gabriel J. C.; FERNANDES, Márcia A.; AMIEL, Tel.
Machine Learning in Conversational Analysis to Recommend Collaboration in Discussion Forums. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1904-1917.
DOI: https://doi.org/10.5753/sbie.2024.242651.
