VITA - Estimando a satisfação de estudantes por meio da Análise de Sentimentos
Resumo
Universidades públicas ao redor do mundo enfrentam um grave e recorrente problema: o alto índice de evasão de alunos em cursos de graduação. Excluindo-se questões vocacionais e financeiras, diversas causas são apontadas para tal problema, como a qualidade docente, o baixo desempenho em disciplinas básicas, e também a insatisfação discente, sendo um indicador altamente responsável por sua evasão em curso de nível superior. Nesse sentido, este trabalho propõe um sistema para melhoria na interação aluno-professor, de modo que o docente tenha maior facilidade em identificar e solucionar os problemas que respondem pela insatisfação do aluno. Resultados preliminares indicam sucesso na melhoria da percepção do professor para com o aluno.
Palavras-chave:
satisfação do aluno, evasão, interação aluno-professor, análise de sentimentos, ensino superior
Referências
Aung, K. Z. and Myo, N. N. (2017). Sentiment analysis of students’ comment using lexicon based approach. In IEEE/ACIS International Conference on Computer and Information Science (ICIS), pages 149–154.
Balahadia, F. F., Fernando, M. C. G., and Juanatas, I. C. (2016). Teacher’s performance evaluation tool using opinion mining with sentiment analysis. In IEEE Region 10 Symposium (TENSYMP), pages 95–98.
Barrón-Estrada, M. L., Zatarain-Cabada, R., Oramas-Bustillos, R., and González-Hernández, F. (2017). Sentiment analysis in an affective intelligent tutoring system. In IEEE International Conference on Advanced Learning Technologies (ICALT), pages 394–397.
Bradley, M. M. and Lang, P. J. (1999). Affective Norms for English Words (ANEW): Instruction manual and affective ratings.
Chen, Y.-L., Hu, H.-W., and Tang, K. (2009). Constructing a decision tree from data with hierarchical class labels. Expert Syst. Appl., 36(3):4838–4847.
de Souza, S. A. and Reinert, J. N. (2010). Avaliação de um curso de ensino superior através da satisfação/insatisfação discente. Avaliação: Revista da Avaliação da Educação Superior, 15(1).
Elliott, K. M. and Shin, D. (2002). Student satisfaction: An alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2):197–209.
Filho, P. P. B., Pardo, T. A. S., and Aluísio, R. M. (2013). An evaluation of the Brazilian Portuguese LIWC dictionary for sentiment analysis. In Brazilian Symposium in Information and Human Language Technology, pages 215–219.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013). Comparing and combining sentiment analysis methods. In Proceedings of the First ACM Conference on Online Social Networks, pages 27–38.
Hassenzahl, M. (2008). User experience (UX): Towards an experiential perspective on product quality. In Proceedings of the 20th Conference on L’Homme-Machine, pages 11–15.
Hauke, J. and Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. 30:87–93.
Kao, A. and Poteet, S. R. (2007). Natural language processing and text mining. Springer Science & Business Media.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
Manhães, L. M. B., da Cruz, S. M. S., Costa, R. J. M., Zavaleta, J., and Zimbrão, G. (2011). Previsão de estudantes com risco de evasão utilizando técnicas de mineração de dados. In Brazilian Symposium on Computers in Education (SBIE), volume 1.
Ortigosa, A., Martin, J., and Carro, R. (2014). Sentiment analysis in Facebook and its application to e-learning. 31:527–541.
Pong-Inwong, C. and Kaewmak, K. (2016). Improved sentiment analysis for teaching evaluation using feature selection and voting ensemble learning integration. In IEEE International Conference on Computer and Communications (ICCC), pages 1222–1225.
Rani, S. and Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5):36–43.
Robinson, R. L., Navea, R., and Ickes, W. (2013). Predicting final course performance from students’ written self-introductions: A LIWC analysis. Journal of Language and Social Psychology, 32(4):469–479.
Rodrigues, R. G. A. and Guedes, G. P. (2017). A hybrid affective lexicon for Brazilian Portuguese. In Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, pages 52:1–52:4.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. 18:1121–1133.
Seldin, P. (1993). The use and abuse of student ratings of professors. The Chronicle of Higher Education.
Tausczik, Y. R. and Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54.
Vallejos, C. and Steel, M. (2014). Bayesian survival modelling of university outcomes.
Weerasinghe, I. S., Lalitha, R., and Fernando, S. (2017). Students satisfaction in higher education literature review. American Journal of Educational Research, 5(5):533–539.
Young, H. P. (1988). Condorcet’s theory of voting. American Political Science Review, 82(4):1231–1244.
Yu, L.-C., Lee, L.-H., Hao, S., Wang, J., He, Y., Hu, J., Lai, K. R., and Zhang, X. (2016). Building Chinese affective resources in valence-arousal dimensions. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 540–545. Association for Computational Linguistics.
Balahadia, F. F., Fernando, M. C. G., and Juanatas, I. C. (2016). Teacher’s performance evaluation tool using opinion mining with sentiment analysis. In IEEE Region 10 Symposium (TENSYMP), pages 95–98.
Barrón-Estrada, M. L., Zatarain-Cabada, R., Oramas-Bustillos, R., and González-Hernández, F. (2017). Sentiment analysis in an affective intelligent tutoring system. In IEEE International Conference on Advanced Learning Technologies (ICALT), pages 394–397.
Bradley, M. M. and Lang, P. J. (1999). Affective Norms for English Words (ANEW): Instruction manual and affective ratings.
Chen, Y.-L., Hu, H.-W., and Tang, K. (2009). Constructing a decision tree from data with hierarchical class labels. Expert Syst. Appl., 36(3):4838–4847.
de Souza, S. A. and Reinert, J. N. (2010). Avaliação de um curso de ensino superior através da satisfação/insatisfação discente. Avaliação: Revista da Avaliação da Educação Superior, 15(1).
Elliott, K. M. and Shin, D. (2002). Student satisfaction: An alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2):197–209.
Filho, P. P. B., Pardo, T. A. S., and Aluísio, R. M. (2013). An evaluation of the Brazilian Portuguese LIWC dictionary for sentiment analysis. In Brazilian Symposium in Information and Human Language Technology, pages 215–219.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013). Comparing and combining sentiment analysis methods. In Proceedings of the First ACM Conference on Online Social Networks, pages 27–38.
Hassenzahl, M. (2008). User experience (UX): Towards an experiential perspective on product quality. In Proceedings of the 20th Conference on L’Homme-Machine, pages 11–15.
Hauke, J. and Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. 30:87–93.
Kao, A. and Poteet, S. R. (2007). Natural language processing and text mining. Springer Science & Business Media.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
Manhães, L. M. B., da Cruz, S. M. S., Costa, R. J. M., Zavaleta, J., and Zimbrão, G. (2011). Previsão de estudantes com risco de evasão utilizando técnicas de mineração de dados. In Brazilian Symposium on Computers in Education (SBIE), volume 1.
Ortigosa, A., Martin, J., and Carro, R. (2014). Sentiment analysis in Facebook and its application to e-learning. 31:527–541.
Pong-Inwong, C. and Kaewmak, K. (2016). Improved sentiment analysis for teaching evaluation using feature selection and voting ensemble learning integration. In IEEE International Conference on Computer and Communications (ICCC), pages 1222–1225.
Rani, S. and Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5):36–43.
Robinson, R. L., Navea, R., and Ickes, W. (2013). Predicting final course performance from students’ written self-introductions: A LIWC analysis. Journal of Language and Social Psychology, 32(4):469–479.
Rodrigues, R. G. A. and Guedes, G. P. (2017). A hybrid affective lexicon for Brazilian Portuguese. In Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, pages 52:1–52:4.
Rude, S., Gortner, E.-M., and Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. 18:1121–1133.
Seldin, P. (1993). The use and abuse of student ratings of professors. The Chronicle of Higher Education.
Tausczik, Y. R. and Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54.
Vallejos, C. and Steel, M. (2014). Bayesian survival modelling of university outcomes.
Weerasinghe, I. S., Lalitha, R., and Fernando, S. (2017). Students satisfaction in higher education literature review. American Journal of Educational Research, 5(5):533–539.
Young, H. P. (1988). Condorcet’s theory of voting. American Political Science Review, 82(4):1231–1244.
Yu, L.-C., Lee, L.-H., Hao, S., Wang, J., He, Y., Hu, J., Lai, K. R., and Zhang, X. (2016). Building Chinese affective resources in valence-arousal dimensions. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 540–545. Association for Computational Linguistics.
Publicado
29/10/2018
Como Citar
PREUSS, Leonardo; SANTOS, Joel dos; GUEDES, Gustavo Paiva.
VITA - Estimando a satisfação de estudantes por meio da Análise de Sentimentos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1143-1152.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1143.
