Identifying pedagogical intervention in MOOCs learning processes: a conversational agent proposal
Resumo
Monitoring students in virtual learning environments can be a time-consuming task. Professors and tutors must accompany students in an agile manner. During the COVID-19 pandemic, the use of discussion forums posed new challenges. This work proposes a conversational agent to automatically detect which pedagogical intervention is necessary to guide students in MOOCs environments. Through the attributes of the students' post messages, it is possible to classify which action will be carried out by the agent, applying specific dialogue patterns. In some more specific cases, the tutor's attention is immediately requested. The proposal was evaluated through a feasibility study to verify if semantic detection can contribute to guide the intervention process. According to the results, it is possible to support the tutor, as only 35.2% of interactions required the tutor's action.
Palavras-chave:
Conversational agent, Semantic Rules, Ontology, Pedagogical Intervention, MOOCs
Referências
Agrawal, A., Venkatraman, J., Leonard, S., and Paepcke, A. (2015). Youedu: addressing confusion in mooc discussion forums by recommending instructional video clips.
Bóbó, M., Campos, F., Ströele, V., David, J., and Braga, R. (2019). Identificação do perfil emocional do aluno através de análise de sentimento: Combatendo a evasão escolar. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 30, page 1431.
Braz, F., Campos, F., Ströele, V., and Dantas, M. (2019). An early warning model for school dropout: A case study in e-learning class. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 30,page 1441.
Brigui-Chtioui, I., Caillou, P., and Negre, E. (2017). Intelligent digital learning: Agent-based recommender system. In Proceedings of the 9th International Conference on Machine Learning and Computing, pages 71–76.
Caldiera, V. R. B.-G. and Rombach, H. D. (1994). Goal question metric paradigm. Encyclopedia of software engineering, 1:528–532.
Callaghan, M. J., Bengloan, G., Ferrer, J., Cherel, L., El Mostadi, M. A., Eguíluz, A. G.,and McShane, N. (2018). Voice driven virtual assistant tutor in virtual reality for electronic engineering remote laboratories. In International Conference on Remote Engineering and Virtual Instrumentation, pages 570–580. Springer.
Capuano, N. and Caballé, S. (2015). Towards adaptive peer assessment for moocs. In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pages 64–69. IEEE.
Capuano, N. and Caballé, S. (2019). Multi-attribute categorization of mooc forum posts and applications to conversational agents. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pages 505–514. Springer.
Demetriadis, S., Caballé, S., Papadopoulos, P. M., Gómez-Sánchez, E., Kolling, A., Tegos, S., Tsiatsos, T., Psathas, G., Michos, K., Weinberger, A., et al. (2021). Conversational agents in moocs: reflections on first outcomes of the colmooc project.
Demetriadis, S., Tegos, S., Psathas, G., Tsiatsos, T., Weinberger, A., Caballé, S., Dimitri-adis, Y., Sánchez, E. G., Papadopoulos, P. M., and Karakostas, A. (2018). Conversational agents as group-teacher interaction mediators in moocs. In 2018 Learning With MOOCS (LWMOOCS), pages 43–46. IEEE.
dos Santos, R. P. (2016). Managing and monitoring software ecosystem to support demand and solution analysis. PhD thesis, Universidade Federal do Rio de Janeiro.
Harrathi, M., Touzani, N., and Braham, R. (2017). A hybrid knowlegde-based approach for recommending massive learning activities. In2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pages 49–54. IEEE.
Neves, F., Campos, F., Ströele, V., Capretz, M. A., Jennings, M., Bryant, D., and Dantas,M. (2020). Heath-prior: An intelligent ensemble architecture to identify risk cases in healthcare. IEEE Access, 8:217150–217168.
Rossi, D., Ströele, V., Braga, R., Caballé, S., Capuano, N., Campos, F., Dantas, M.,Lomasto, L., and Toti, D. (2021). Caers: A conversational agent for intervention in moocs’ learning process. In The learning ideas conference.
Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., and Caballé, S. (2020). Detection of student engagement in e-learning systems based on semantic analysis and machine learning. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pages 211–223. Springer.
Bóbó, M., Campos, F., Ströele, V., David, J., and Braga, R. (2019). Identificação do perfil emocional do aluno através de análise de sentimento: Combatendo a evasão escolar. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 30, page 1431.
Braz, F., Campos, F., Ströele, V., and Dantas, M. (2019). An early warning model for school dropout: A case study in e-learning class. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 30,page 1441.
Brigui-Chtioui, I., Caillou, P., and Negre, E. (2017). Intelligent digital learning: Agent-based recommender system. In Proceedings of the 9th International Conference on Machine Learning and Computing, pages 71–76.
Caldiera, V. R. B.-G. and Rombach, H. D. (1994). Goal question metric paradigm. Encyclopedia of software engineering, 1:528–532.
Callaghan, M. J., Bengloan, G., Ferrer, J., Cherel, L., El Mostadi, M. A., Eguíluz, A. G.,and McShane, N. (2018). Voice driven virtual assistant tutor in virtual reality for electronic engineering remote laboratories. In International Conference on Remote Engineering and Virtual Instrumentation, pages 570–580. Springer.
Capuano, N. and Caballé, S. (2015). Towards adaptive peer assessment for moocs. In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pages 64–69. IEEE.
Capuano, N. and Caballé, S. (2019). Multi-attribute categorization of mooc forum posts and applications to conversational agents. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pages 505–514. Springer.
Demetriadis, S., Caballé, S., Papadopoulos, P. M., Gómez-Sánchez, E., Kolling, A., Tegos, S., Tsiatsos, T., Psathas, G., Michos, K., Weinberger, A., et al. (2021). Conversational agents in moocs: reflections on first outcomes of the colmooc project.
Demetriadis, S., Tegos, S., Psathas, G., Tsiatsos, T., Weinberger, A., Caballé, S., Dimitri-adis, Y., Sánchez, E. G., Papadopoulos, P. M., and Karakostas, A. (2018). Conversational agents as group-teacher interaction mediators in moocs. In 2018 Learning With MOOCS (LWMOOCS), pages 43–46. IEEE.
dos Santos, R. P. (2016). Managing and monitoring software ecosystem to support demand and solution analysis. PhD thesis, Universidade Federal do Rio de Janeiro.
Harrathi, M., Touzani, N., and Braham, R. (2017). A hybrid knowlegde-based approach for recommending massive learning activities. In2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pages 49–54. IEEE.
Neves, F., Campos, F., Ströele, V., Capretz, M. A., Jennings, M., Bryant, D., and Dantas,M. (2020). Heath-prior: An intelligent ensemble architecture to identify risk cases in healthcare. IEEE Access, 8:217150–217168.
Rossi, D., Ströele, V., Braga, R., Caballé, S., Capuano, N., Campos, F., Dantas, M.,Lomasto, L., and Toti, D. (2021). Caers: A conversational agent for intervention in moocs’ learning process. In The learning ideas conference.
Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., and Caballé, S. (2020). Detection of student engagement in e-learning systems based on semantic analysis and machine learning. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pages 211–223. Springer.
Publicado
22/11/2021
Como Citar
ROSSI, Diego; STRÖELE, Victor; CAMPOS, Fernanda; BRAGA, Regina; DAVID, José Maria N..
Identifying pedagogical intervention in MOOCs learning processes: a conversational agent proposal. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 32. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 849-860.
DOI: https://doi.org/10.5753/sbie.2021.218441.
