Classificação de Interações com Indicadores de Engajamento dos Estudantes no Aprendizado Online
Resumo
Este estudo aborda a dificuldade de analisar indicadores do engajamento dos estudantes em atividades de ensino-aprendizagem online. Foi analisado o desempenho de diferentes algoritmos de Aprendizagem de Máquina (AM), combinados com estratégias de comitês de classificadores heterogêneos e homogêneos, para identificar as abordagens mais eficazes na previsão dos níveis de interação dos estudantes. Os resultados indicam que o comitê Boosting com os algoritmos Máquina de Vetor de Suporte (MVS) e Árvore de Decisão (AD) apresentaram melhor desempenho. Esta estratégia de AM pode ajudar a identificar indicadores do engajamento em atividades do aprendizado online. Neste sentido, as combinações dos classificadores foram aplicadas para análise e apresentação dos indicadores de interação para apoiar tutores humanos na promoção do engajamento estudantil.
Palavras-chave:
comitês, classificadores, interação, engajamento de estudante
Referências
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Salta, K. et al. Shift from a traditional to a distance learning environment during the COVID-19 pandemic: university students’ engagement and interactions. Science & Education, v. 31, n. 1, p. 93-122. 2022. DOI: 10.1007/s11191-021-00234-x
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Sweller, J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, v. 4, n. 4, p. 295–312. 1994. DOI: 10.1016/0959-4752(94)90003-5
Taser, P. Y. Application of bagging and boosting approaches using decision tree-based algorithms in diabetes risk prediction. In: Proceedings. MDPI. p. 6. 2021. DOI: 10.3390/proceedings2021074006
Wang, G. et al. A comparative assessment of ensemble learning for credit scoring. Expert systems with applications, v. 38, n. 1, p. 223-230, 2011. DOI: 10.1016/j.eswa.2010.06.048
Werang, B. R.; Leba, S. M. R. Factors Affecting Student Engagement in Online Teaching and Learning: A Qualitative Case Study. Qualitative Report, v. 27, n. 2. 2022. DOI: 10.1080/13562517.2017.1319808
Zafari, M. et al. Artificial intelligence applications in K-12 education: A systematic literature review. IEEE Access. 2022. DOI: 10.1109/ACCESS.2022.3179356
Alhazmi, A. K. et al. AI’s Role and Application in Education: Systematic Review. Intelligent Sustainable Systems: Selected Papers of WorldS4 2022, v. 1, p. 1-14. 2023. DOI: 10.1007/978-981-19-7660-5_1
Almarghani, E. M.; Mijatovic, I. Factors affecting student engagement in HEIs-it is all about good teaching. Teaching in higher education, v. 22, n. 8, p. 940-956. 2017. DOI: 10.1080/13562517.2017.1319808
Archambault, L.; Leary, H.; Rice, K. Pillars of online pedagogy: A framework for teaching in online learning environments. Educational Psychologist, v. 57, n. 3, p. 178-191. 2022. DOI: 10.1080/00461520.2022.2051513
Brohi, S. N. et al. Accuracy comparison of machine learning algorithms for predictive analytics in higher education. In: Emerging Technologies in Computing: Second International Conference, iCETiC 2019, London, UK, August 19–20, 2019, Proceedings 2. Springer International Publishing. p. 254-261. 2019. DOI: 10.1007/978-3-030-23943-5_19
Buono, P. et al. Assessing student engagement from facial behavior in on-line learning. Multimedia Tools and Applications, p. 1-19. 2022. DOI: 10.1007/s11042-022-14048-8
Chang, c. et al. An ensemble model using face and body tracking for engagement detection. In: Proceedings of the 20th ACM international conference on multimodal interaction. p. 616-622. 2018. DOI: 10.1145/3242969.3264986
De Bruin, L. R. Collaborative learning experiences in the university jazz/creative music ensemble: Student perspectives on instructional communication. Psychology of Music, v. 50, n. 4, p. 1039-1058. 2022. DOI: 10.1177/03057356211027651
Džeroski, S.; ŽENKO, B. Is combining classifiers with stacking better than selecting the best one?. Machine learning, v. 54, p. 255-273. 2004. DOI: 10.1023/b:mach.0000015881.36452.6e
Echeverria, V. et al. Designing Hybrid Human-AI Orchestration Tools for Individual and Collaborative Activities: A Technology Probe Study. IEEE Transactions on Learning Technologies. 2023. DOI: 10.1109/TLT.2023.3248155
Evangelista, E. A hybrid machine learning framework for predicting students’ performance in virtual learning environment. International Journal of Emerging Technologies in Learning (iJET), v. 16, n. 24, p. 255-272. 2021. DOI: 10.3991/ijet.v16i24.26151
Evangelista, E.; SY, B. D. An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods. International Journal of Electrical and Computer Engineering, v. 12, n. 5, p. 5226. 2022. DOI: 10.11591/ijece.v12i5.pp5226-5235
Kamath, S. et al. Engagement analysis of students in online learning environments. In: Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021). Springer International Publishing. p. 34-47. 2022. DOI: 10.1007/978-3-030-82469-3_4
Kim, J.; Davis, T.; Hong, L. Augmented Intelligence: Enhancing Human Decision Making. In: Bridging Human Intelligence and Artificial Intelligence. Cham: Springer International Publishing. p. 151-170. 2022. DOI: 10.1007/978-3-030-84729-6_10
Mayer, R. E. Multimedia learning (2nd ed.). Cambridge University Press. 2009.
Moraitis, N. et al. On the assessment of ensemble models for propagation loss forecasts in rural environments. IEEE Wireless Communications Letters, v. 11, n. 5, p. 1097-1101. 2022. DOI: 10.1109/LWC.2022.3157520
Muhlbaier, M. D.; Topalis, A.; Polikar, R. Learn, N. C. Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes. IEEE transactions on neural networks, v. 20, n. 1, p. 152-168, 2008. DOI: 10.1109/TNN.2008.2008326
Nti, I. K.; Adekoya, A. F.; Weyori, B. A. A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, v. 7, n. 1, p. 1-40. 2020. DOI: 10.1186/s40537-020-00299-5
Pereira, A. J.; Gomes, A. S.; Primo, T. T. Especificação de Sistema de Recomendação Educacional de Incentivo as Interações em Plataforma Social de Aprendizagem. RENOTE, v. 20, n. 2, p. 1-10. 2022. DOI: 10.22456/1679-1916.129143
Pereira, A. J. et al. Learning Mediated by Social Network for Education in K-12: Levels of Interaction, Strategies, and Difficulties. Education Sciences, v. 13, n. 2, p. 100. 2023. DOI: 10.3390/educsci13020100
Pereira, A. J.; Gomes, A. S.; Primo, T. Uma Abordagem de Sistema de Tutoria Inteligente para Cooperação com a Atuação de Tutores Humanos. RENOTE, Porto Alegre, v. 21, n. 2, p. 208–219, 2023. [link]
Refaeilzadeh, P., Tang, L., Liu, H. Cross-Validation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. 2009. DOI: 10.1007/978-0-387-39940-9_565
Rokach, L. Ensemble-based classifiers. Artificial intelligence review, v. 33, p. 1-39. 2010. DOI: 10.1007/s10462-009-9124-7
Srinivasa, KG; Kurni, M.; Saritha, K. Aproveitando o poder da IA para a educação. In: Métodos de aprendizagem, ensino e avaliação para aprendizes contemporâneos: pedagogia para a geração digital. Cingapura: Springer Nature Cingapura. p. 311-342. 2022. DOI: 10.1007/978-981-19-6734-4_13
Saleem, F. et al. Intelligent decision support system for predicting student’s E-learning performance using ensemble machine learning. Mathematics, v. 9, n. 17, p. 2078. 2021. DOI: 10.3390/math9172078
Saleh, K.; YU, K.; Chen, F. Video-Based Student Engagement Estimation via Time Convolution Neural Networks for Remote Learning. In: AI 2021: Advances in Artificial Intelligence: 34th Australasian Joint Conference, AI 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings. Cham: Springer International Publishing. p. 658-667. 2022. DOI: 10.1007/978-3-030-97546-3_53
Salta, K. et al. Shift from a traditional to a distance learning environment during the COVID-19 pandemic: university students’ engagement and interactions. Science & Education, v. 31, n. 1, p. 93-122. 2022. DOI: 10.1007/s11191-021-00234-x
St-Hilaire, F. et al. A New era: Intelligent tutoring systems will transform online learning for millions. arXiv preprint arXiv:2203.03724. 2022. DOI: 10.48550/arXiv.2203.03724
Sweller, J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, v. 4, n. 4, p. 295–312. 1994. DOI: 10.1016/0959-4752(94)90003-5
Taser, P. Y. Application of bagging and boosting approaches using decision tree-based algorithms in diabetes risk prediction. In: Proceedings. MDPI. p. 6. 2021. DOI: 10.3390/proceedings2021074006
Wang, G. et al. A comparative assessment of ensemble learning for credit scoring. Expert systems with applications, v. 38, n. 1, p. 223-230, 2011. DOI: 10.1016/j.eswa.2010.06.048
Werang, B. R.; Leba, S. M. R. Factors Affecting Student Engagement in Online Teaching and Learning: A Qualitative Case Study. Qualitative Report, v. 27, n. 2. 2022. DOI: 10.1080/13562517.2017.1319808
Zafari, M. et al. Artificial intelligence applications in K-12 education: A systematic literature review. IEEE Access. 2022. DOI: 10.1109/ACCESS.2022.3179356
Publicado
04/11/2024
Como Citar
PEREIRA, Aluisio José; GOMES, Alex Sandro; PRIMO, Tiago Thompsen.
Classificação de Interações com Indicadores de Engajamento dos Estudantes no Aprendizado Online. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1574-1586.
DOI: https://doi.org/10.5753/sbie.2024.242141.
