Consensocomo Indicativo de Qualidade em Fóruns Educacionais: Uma Análise no Âmbito de MOOC
Resumo
Discussões em chats e fóruns de plataformas de ensino online, como aquelas para Massive Open Online Courses - MOOC, servem para construir conclusões consensuais que agreguem valor pedagógico. O estudo da correlação entre consenso e qualidade da conclusão em discussões online tem recebido pouca atenção. Este artigo usa o "Soft Consensus" - quando há graduação do consenso, desde nenhum até unanimidade - para apresentar um estudo estatístico onde tal correlação é medida a partir de dados de fóruns de MOOC e o apoio dos instrutores à conclusão consensual é o indicador de qualidade. Resultados preliminares apontam para uma correlação negativa.
Palavras-chave:
Consensocomo, Qualidade, Fóruns Educacionais, MOOC
Referências
Agrawal, A., Venkatraman, J., Leonard, S., and Paepcke, A. (2015). Youedu: addressing confusion in MOOC discussion forums by recommending instructional video clips.
Alonso, S., Pérez, I. J., Cabrerizo, F. J., and Herrera-Viedma, E. (2013). A linguistic consensus model for web 2.0 communities. Applied Soft Computing, 13(1):149–157.
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Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., and Wong, F. M. F. (2014). Learning about social learning in “moocs”: From statistical analysis to generative model. IEEE transactions on Learning Technologies, 7(4):346–359.
Cabrerizo, F. J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A. S., and Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Journal of Intelligent & Fuzzy Systems, 29(3):1109–1118.
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Gelman, A. and Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Gomaa, W. H. and Fahmy, A. A. (2013). A survey of text similarity approaches. International Journal of Computer Applications, 68(13):13–18.
Hiray, S. and Duppada, V. (2017). Agree to disagree: Improving disagreement detection with dual grus. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pages 147–152. IEEE.
Hirokawa, R. Y. (1982). Consensus group decision-making, quality of decision, and group satisfaction: An attempt to sort “fact” from “fiction”. Communication Studies, 33(2):407–415.
Janis, I. L. and Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. Free press.
Johnson, D. W. and Johnson, R. T. (1979). Conflict in the classroom: Controversy and learning. Review of educational research, 49(1):51–69.
Kolb, P. (2008). Disco: A multilingual database of distributionally similar words. Proceedings of KONVENS-2008, Berlin, 156.
Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707–710.
Lin, D. (1998). Automatic retrieval and clustering of similar words. In COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics, volume 2.
Miller, G. (1998). WordNet: An electronic lexical database. MIT press.
Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y., Chiclana, F., and Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459:20–35.
Potash, P. and Rumshisky, A. (2017). Towards debate automation: A recurrent model for predicting debate winners. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2465–2475.
Qiu, M. and Jiang, J. (2013). A latent variable model for viewpoint discovery from threaded forum posts. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1031–1040.
Rosenthal, S. and McKeown, K. (2015). I couldn’t agree more: The role of conversational structure in agreement and disagreement detection in online discussions. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 168–177.
Seerat, B. and Azam, F. (2012). Opinion mining: Issues and challenges (a survey). International Journal of Computer Applications, 49(9).
Tolmie, P., Procter, R., Rouncefield, M., Liakata, M., and Zubiaga, A. (2018). Microblog analysis as a program of work. ACM Transactions on Social Computing, 1(1):2[1].
Trimbur, J. (1989). Consensus and difference in collaborative learning. College English, 51(6):602–616.
Van Knippenberg, D., De Dreu, C. K., and Homan, A. C. (2004). Work group diversity and group performance: an integrative model and research agenda. Journal of applied psychology, 89(6):1008.
Vinodhini, G. and Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6):282–292.
Zimmerman, B. J. and Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.
Zubiaga, A., Kochkina, E., Liakata, M., Procter, R., and Lukasik, M. (2016). Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. In Proceedings of COLING, the International Conference on Computational Linguistics, pages 2438–2448.
Alonso, S., Pérez, I. J., Cabrerizo, F. J., and Herrera-Viedma, E. (2013). A linguistic consensus model for web 2.0 communities. Applied Soft Computing, 13(1):149–157.
Bass, B. M. (1963). Amount of participation, coalescence, and profitability of decision making discussions. The Journal of Abnormal and Social Psychology, 67(1):92.
Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., and Wong, F. M. F. (2014). Learning about social learning in “moocs”: From statistical analysis to generative model. IEEE transactions on Learning Technologies, 7(4):346–359.
Cabrerizo, F. J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A. S., and Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Journal of Intelligent & Fuzzy Systems, 29(3):1109–1118.
Damerau, F. J. (1964). A technique for computer detection and correction of spelling errors. Communications of the ACM, 7(3):171–176.
Doise, W., Mugny, G., James, A. S., Emler, N., and Mackie, D. (2013). The social development of the intellect, volume 10. Elsevier.
Gelman, A. and Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Gomaa, W. H. and Fahmy, A. A. (2013). A survey of text similarity approaches. International Journal of Computer Applications, 68(13):13–18.
Hiray, S. and Duppada, V. (2017). Agree to disagree: Improving disagreement detection with dual grus. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pages 147–152. IEEE.
Hirokawa, R. Y. (1982). Consensus group decision-making, quality of decision, and group satisfaction: An attempt to sort “fact” from “fiction”. Communication Studies, 33(2):407–415.
Janis, I. L. and Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. Free press.
Johnson, D. W. and Johnson, R. T. (1979). Conflict in the classroom: Controversy and learning. Review of educational research, 49(1):51–69.
Kolb, P. (2008). Disco: A multilingual database of distributionally similar words. Proceedings of KONVENS-2008, Berlin, 156.
Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707–710.
Lin, D. (1998). Automatic retrieval and clustering of similar words. In COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics, volume 2.
Miller, G. (1998). WordNet: An electronic lexical database. MIT press.
Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y., Chiclana, F., and Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459:20–35.
Potash, P. and Rumshisky, A. (2017). Towards debate automation: A recurrent model for predicting debate winners. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2465–2475.
Qiu, M. and Jiang, J. (2013). A latent variable model for viewpoint discovery from threaded forum posts. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1031–1040.
Rosenthal, S. and McKeown, K. (2015). I couldn’t agree more: The role of conversational structure in agreement and disagreement detection in online discussions. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 168–177.
Seerat, B. and Azam, F. (2012). Opinion mining: Issues and challenges (a survey). International Journal of Computer Applications, 49(9).
Tolmie, P., Procter, R., Rouncefield, M., Liakata, M., and Zubiaga, A. (2018). Microblog analysis as a program of work. ACM Transactions on Social Computing, 1(1):2[1].
Trimbur, J. (1989). Consensus and difference in collaborative learning. College English, 51(6):602–616.
Van Knippenberg, D., De Dreu, C. K., and Homan, A. C. (2004). Work group diversity and group performance: an integrative model and research agenda. Journal of applied psychology, 89(6):1008.
Vinodhini, G. and Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6):282–292.
Zimmerman, B. J. and Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.
Zubiaga, A., Kochkina, E., Liakata, M., Procter, R., and Lukasik, M. (2016). Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. In Proceedings of COLING, the International Conference on Computational Linguistics, pages 2438–2448.
Publicado
11/11/2019
Como Citar
CLEMENTINO, Tiago L. P.; MOURA, José Antão B..
Consensocomo Indicativo de Qualidade em Fóruns Educacionais: Uma Análise no Âmbito de MOOC. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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p. 1965-1974.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1965.
