Automatic classification of subjective attributes from student messages in virtual learning environments
ResumoAccompanying students in virtual learning environments to identify those who need help is a difficult and time-consuming task. Identifying subjective attributes that recognize students feelings can help teachers and tutors in pedagogical interventions. The interaction must motivate and keep students engaged. This article proposes an architecture capable of automatically carrying out some pedagogical intervention. The architecture uses automatic textual classification models to detect multiple attributes in post messages, such as Sentiment, Post Type, Urgency, and Confusion. The main goal is to predict learning problems and act to minimize their impacts. The evaluation was performed with data from the Stanford MOOCPosts Dataset to verify if the models allow the automatic classification of subjective attributes. Our results show that the proposal outperforms other approaches in this dataset.
Bóbó, M., Campos, F., Stroele, 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., Stroele, 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.
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.
Capuano, N., Caballé, S., Conesa, J., and Greco, A. (2021). Attention-based hierarchical recurrent neural networks for mooc forum posts analysis. Journal of Ambient Intelligence and Humanized Computing, 12(11):9977–9989.
Chaturvedi, S., Goldwasser, D., and Daumé III, H. (2014). Predicting instructor’s intervention in mooc forums. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1501–1511.
Cuevas, R., Ntoumanis, N., Fernandez-Bustos, J. G., and Bartholomew, K. (2018). Does teacher evaluation based on student performance predict motivation, well-being, and ill-being? Journal of school psychology, 68:154–162.
Cunningham, P., Cord, M., and Delany, S. J. (2008). Supervised learning. In Cord, M. and Cunningham, P., editors, Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval, pages 21–49, Berlin, Heidelberg. Springer Berlin Heidelberg.
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. Intelligent Systems and Learning Data Analytics in Online Education, pages xxxvii–lxxiv.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Fandiño, F. G. E. and Velandia, A. J. S. (2020). How an online tutor motivates e-learning english. Heliyon, 6(8):e04630.
Gomes, J., de Mello, R. C., Ströele, V., and de Souza, J. F. (2022). A hereditary attentive template-based approach for complex knowledge base question answering systems. Expert Systems with Applications, page 117725.
Khanal, S. S., Prasad, P., Alsadoon, A., and Maag, A. (2020). A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4):2635–2664.
Khodeir, N. A. (2021). Bi-gru urgent classification for mooc discussion forums based on bert. IEEE Access, 9:58243–58255.
Marbouti, F., Diefes-Dux, H. A., and Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103:1–15.
Moreno-Guerrero, A.-J., Aznar-Díaz, I., Cáceres-Reche, P., and Alonso-García, S. (2020). E-learning in the teaching of mathematics: An educational experience in adult high school. Mathematics, 8(5):840.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., Estévez-Ayres, I., and Kloos, C. D. (2018a). Sentiment analysis in moocs: A case study. In 2018 IEEE Global Engineering Education Conference (EDUCON), pages 1489–1496. IEEE.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., and Kloos, C. D. (2018b). Prediction in moocs: A review and future research directions. IEEE Transactions on Learning Technologies, 12(3):384–401.
Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., and Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4):233–241.
Panigrahi, R., Srivastava, P. R., and Panigrahi, P. K. (2020). Effectiveness of e-learning: the mediating role of student engagement on perceived learning effectiveness. Information Technology & People, 34(7):1840–1862.
Rossi, D., Ströele, V., Campos, F., Braga, R., and David, J. M. N. (2021). Identifying pedagogical intervention in moocs learning processes: a conversational agent proposal. In Anais do XXXII Simpósio Brasileiro de Informática na Educação, pages 849–860. SBC.
Tai, K. S., Socher, R., and Manning, C. D. (2015). Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075.
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.
Yang, D., Wen, M., Howley, I., Kraut, R., and Rose, C. (2015). Exploring the effect of confusion in discussion forums of massive open online courses. In Proceedings of the second (2015) ACM conference on learning@ scale, pages 121–130.