Usability Perspective of an Authoring Solution to Assist Pedagogical Decision-Making
Resumo
There is a quest to provide education from anywhere, at any time and for anyone, using digital information and communication technologies, but there is no equivalent increase in support for the instructors responsible for maintaining such courses, evidenced by the large number of dropouts and failures. We propose an authoring solution to guide pedagogical decision-making in on-line learning environments to help instructors (1) to discover pedagogical situations occurring in their courses; (2) understand these situation; (3) make decisions to address them; (4) monitor and evaluate the impact from decisions made. However, instructors do not master these abilities, nor is it practical/appropriate to ask them to do so. We developed a proof-of-concept version of an authoring solution, named T-Partner, that guides instructors through these 4 steps. We conducted an experiment to evaluate whether it is perceived as useful and easy to use. The results show that its usefulness and ease of use were positively perceived by the instructors.
Palavras-chave:
pedagogical decision-making process, data-based decisions, authoring tools, online learning environments
Referências
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Bittencourt, I.I., Costa, E., Silva, M., and Soares, E. (2009). A computational model for developing semantic web-based educational systems. Knowledge-Based Systems, 22(4): 302–315.
Cheesman, J. and Daniels, J. (2000). UML components: a simple process for specifying component-based software. Addison-Wesley Longman Publishing Co., Inc.
Chou, C.-Y., Huang, B.-H., and Lin, C.-J. (2011). Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. Computers & Education, 57(4): 2303–2312.
Chrysafiadi, K. and Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11): 4715–4729.
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Heffernan, N.T. and Heffernan, C.L. (2014). The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4): 470–497.
Kopcha, T.J. (2012). Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4): 1109–1121.
Liyanagunawardena, T.R., Parslow, P., and Williams, S. (2014). Dropout: Mooc participants' perspective.
Mandinach, E.B. and Jackson, S.S. (2012). Transforming teaching and learning through data-driven decision making. Corwin Press.
Onah, D.F., Sinclair, J., and Boyatt, R. (2014). Dropout rates of massive open online courses: behavioral patterns. EDULEARN14 Proceedings, pages 5825–5834.
Paiva, R., Bittencourt, I.I., Tenório, T., Jaques, P., and Isotani, S. (2016). What do students do online? modeling students' interactions to improve their learning experience. Computers in Human Behavior, 64: 769–781.
Romero, C. and Ventura, S. (2016). Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
Schildkamp, K., Lai, M.K., and Earl, L. (2012). Data-based decision making in education: Challenges and opportunities, volume 17. Springer Science & Business Media.
Siemens, G. and dBaker, R.S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge, pages 252–254. ACM.
Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4): 2432–2440.
Belanger, Y. and Thornton, J. (2013). Bioelectricity: A quantitative approach duke university's first mooc.
Bienkowski, M., Feng, M., and Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, pages 1–57.
Bittencourt, I.I., Costa, E., Silva, M., and Soares, E. (2009). A computational model for developing semantic web-based educational systems. Knowledge-Based Systems, 22(4): 302–315.
Cheesman, J. and Daniels, J. (2000). UML components: a simple process for specifying component-based software. Addison-Wesley Longman Publishing Co., Inc.
Chou, C.-Y., Huang, B.-H., and Lin, C.-J. (2011). Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. Computers & Education, 57(4): 2303–2312.
Chrysafiadi, K. and Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11): 4715–4729.
de Educação a Distância, A.B. (2016). Censoeadbr (2015). Relatório Analítico da Aprendizagem a Distância no Brasil.
Heffernan, N.T. and Heffernan, C.L. (2014). The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4): 470–497.
Kopcha, T.J. (2012). Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4): 1109–1121.
Liyanagunawardena, T.R., Parslow, P., and Williams, S. (2014). Dropout: Mooc participants' perspective.
Mandinach, E.B. and Jackson, S.S. (2012). Transforming teaching and learning through data-driven decision making. Corwin Press.
Onah, D.F., Sinclair, J., and Boyatt, R. (2014). Dropout rates of massive open online courses: behavioral patterns. EDULEARN14 Proceedings, pages 5825–5834.
Paiva, R., Bittencourt, I.I., Tenório, T., Jaques, P., and Isotani, S. (2016). What do students do online? modeling students' interactions to improve their learning experience. Computers in Human Behavior, 64: 769–781.
Romero, C. and Ventura, S. (2016). Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
Schildkamp, K., Lai, M.K., and Earl, L. (2012). Data-based decision making in education: Challenges and opportunities, volume 17. Springer Science & Business Media.
Siemens, G. and dBaker, R.S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge, pages 252–254. ACM.
Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4): 2432–2440.
Publicado
30/10/2017
Como Citar
PAIVA, Ranilson; BITTENCOURT, Ig Ibert; VINICIUS, André; AMORIM, Sérgio; LEMOS, Wansel; DERMEVAL, Diego; ISOTANI, Seiji.
Usability Perspective of an Authoring Solution to Assist Pedagogical Decision-Making. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 28. , 2017, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1587-1596.
DOI: https://doi.org/10.5753/cbie.sbie.2017.1587.
