A Comparative Analysis Between Good Feedback Descriptors on Online Courses
Resumo
Feedback is a critical component of the teaching-learning process. Through it, teachers share relevant information so that students understand the subjects and activities, in addition to promoting self-regulation. However, the activity of writing and sharing feedback is not easy and may even lead to students’ demotivation. Given this, it is possible to find several works in the educational research literature that propose good practices for elaborating textual feedback. This work aims to analyze the relationship between different models of good practices for feedback and the relationship between English feedback and Portuguese feedback using an epistemic network (ENA). The results show that some characteristics are similar, but the models do not have a direct relationship. It is concluded that a combination of these models can improve the analysis of feedback quality.Referências
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Cavalcanti, A. P., de Mello, R. F. L., Rolim, V., André, M., Freitas, F., and Gaševic, D. (2019). An analysis of the use of good feedback practices in online learning courses. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 153–157. IEEE.
Cavalcanti, A. P., Mello, R. F., Miranda, P., Nascimento, A., and Freitas, F. (2021). Utilização de recursos linguísticos para classificação automática de mensagens de feedback. In Anais do XXXII Simpósio Brasileiro de Informática na Educação, pages 861–872. SBC.
Dunworth, K. and Sanchez, H. S. (2016). Perceptions of quality in staff-student written feedback in higher education: a case study. Teaching in Higher Education, 21(5):576–589.
Evans, C. (2013). Making sense of assessment feedback in higher education. Review of educational research, 83(1):70–120.
Ferreira, R., Kovanović, V., Gašević, D., and Rolim, V. (2018). Towards combined network and text analytics of student discourse in online discussions. In International Conference on Artificial Intelligence in Education, pages 111–126. Springer.
Gašević, D., Joksimović, S., Eagan, B. R., and Shaffer, D. W. (2019). Sens: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92:562–577.
Gibbs, G. and Simpson, C. (2005). Conditions under which assessment supports students’ learning. Learning and teaching in higher education, (1):3–31.
Hattie, J. and Gan, M. (2011). Instruction based on feedback. In Handbook of research on learning and instruction, pages 263–285. Routledge.
Hattie, J. and Timperley, H. (2007). The power of feedback. Review of educational research, 77(1):81–112.
Henderson, M., Ajjawi, R., Boud, D., and Molloy, E., editors (2019). The Impact of Feedback in Higher Education: Improving Assessment Outcomes for Learners. Springer International Publishing, Cham, Switzerland. Google-Books-ID: WyxQxgEACAAJ.
Landis, J. R. and Koch, G. G. (1977). An application of hierarchical kappatype statistics in the assessment of majority agreement among multiple observers. Biometrics, pages 363–374.
Langer, P. (2011). The use of feedback in education: a complex instructional strategy. Psychological reports, 109(3):775–784.
Maitra, S., Madan, S., Kandwal, R., and Mahajan, P. (2018). Mining authentic student feedback for faculty using naı̈ve bayes classifier. Procedia computer science, 132:1171–1183.
Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., and Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pages 461–470. ACM.
Mello, R. F. and Gašević, D. (2019). What is the effect of a dominant code in an epistemic network analysis? In International Conference on Quantitative Ethnography, pages 66–76. Springer.
Nicol, D. J. and Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in higher education, 31(2):199–218.
Osakwe, I., Chen, G., Whitelock-Wainwright, A., Gaševic, D., Cavalcanti, A. P., and Mello, R. F. (2021). Towards automated content analysis of feedback: A multilanguage study. In Proceedings of the 14th International Conference on Educational Data Mining.
Parikh, A., McReelis, K., and Hodges, B. (2001). Student feedback in problem based learning: a survey of 103 final year students across five ontario medical schools. Medical education, 35(7):632–636.
Rolim, V., Ferreira, R., Lins, R. D., and Gǎsević, D. (2019). A network-based analytic approach to uncovering the relationship between social and cognitive presences in communities of inquiry. The Internet and Higher Education, 42:53–65.
Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional science, 18(2):119–144.
Sadler, D. R. (2010). Beyond feedback: Developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35(5):535–550.
Shaffer, D. W. (2017). Quantitative ethnography. Cathcart Press, Madison, WI.
Shaffer, D. W., Collier, W., and Ruis, A. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3):9–45.
Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. A., and Mislevy, R. (2009). Epistemic Network Analysis: A Prototype for 21st-Century Assessment of Learning. International Journal of Learning and Media, 1(2):33–53.
Wisniewski, B., Zierer, K., and Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10:3087.
Boud, D. and Falchikov, N. (2007). Rethinking assessment in higher education: Learning for the longer term. Routledge.
Cavalcanti, A. P., de Mello, R. F. L., Rolim, V., André, M., Freitas, F., and Gaševic, D. (2019). An analysis of the use of good feedback practices in online learning courses. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 153–157. IEEE.
Cavalcanti, A. P., Mello, R. F., Miranda, P., Nascimento, A., and Freitas, F. (2021). Utilização de recursos linguísticos para classificação automática de mensagens de feedback. In Anais do XXXII Simpósio Brasileiro de Informática na Educação, pages 861–872. SBC.
Dunworth, K. and Sanchez, H. S. (2016). Perceptions of quality in staff-student written feedback in higher education: a case study. Teaching in Higher Education, 21(5):576–589.
Evans, C. (2013). Making sense of assessment feedback in higher education. Review of educational research, 83(1):70–120.
Ferreira, R., Kovanović, V., Gašević, D., and Rolim, V. (2018). Towards combined network and text analytics of student discourse in online discussions. In International Conference on Artificial Intelligence in Education, pages 111–126. Springer.
Gašević, D., Joksimović, S., Eagan, B. R., and Shaffer, D. W. (2019). Sens: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92:562–577.
Gibbs, G. and Simpson, C. (2005). Conditions under which assessment supports students’ learning. Learning and teaching in higher education, (1):3–31.
Hattie, J. and Gan, M. (2011). Instruction based on feedback. In Handbook of research on learning and instruction, pages 263–285. Routledge.
Hattie, J. and Timperley, H. (2007). The power of feedback. Review of educational research, 77(1):81–112.
Henderson, M., Ajjawi, R., Boud, D., and Molloy, E., editors (2019). The Impact of Feedback in Higher Education: Improving Assessment Outcomes for Learners. Springer International Publishing, Cham, Switzerland. Google-Books-ID: WyxQxgEACAAJ.
Landis, J. R. and Koch, G. G. (1977). An application of hierarchical kappatype statistics in the assessment of majority agreement among multiple observers. Biometrics, pages 363–374.
Langer, P. (2011). The use of feedback in education: a complex instructional strategy. Psychological reports, 109(3):775–784.
Maitra, S., Madan, S., Kandwal, R., and Mahajan, P. (2018). Mining authentic student feedback for faculty using naı̈ve bayes classifier. Procedia computer science, 132:1171–1183.
Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., and Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pages 461–470. ACM.
Mello, R. F. and Gašević, D. (2019). What is the effect of a dominant code in an epistemic network analysis? In International Conference on Quantitative Ethnography, pages 66–76. Springer.
Nicol, D. J. and Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in higher education, 31(2):199–218.
Osakwe, I., Chen, G., Whitelock-Wainwright, A., Gaševic, D., Cavalcanti, A. P., and Mello, R. F. (2021). Towards automated content analysis of feedback: A multilanguage study. In Proceedings of the 14th International Conference on Educational Data Mining.
Parikh, A., McReelis, K., and Hodges, B. (2001). Student feedback in problem based learning: a survey of 103 final year students across five ontario medical schools. Medical education, 35(7):632–636.
Rolim, V., Ferreira, R., Lins, R. D., and Gǎsević, D. (2019). A network-based analytic approach to uncovering the relationship between social and cognitive presences in communities of inquiry. The Internet and Higher Education, 42:53–65.
Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional science, 18(2):119–144.
Sadler, D. R. (2010). Beyond feedback: Developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35(5):535–550.
Shaffer, D. W. (2017). Quantitative ethnography. Cathcart Press, Madison, WI.
Shaffer, D. W., Collier, W., and Ruis, A. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3):9–45.
Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. A., and Mislevy, R. (2009). Epistemic Network Analysis: A Prototype for 21st-Century Assessment of Learning. International Journal of Learning and Media, 1(2):33–53.
Wisniewski, B., Zierer, K., and Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10:3087.
Publicado
06/11/2023
Como Citar
CAVALCANTI, Anderson Pinheiro; ROLIM, Vitor Belarmino; GAŠEVIĆ, Dragan; MELLO, Rafael Ferreira.
A Comparative Analysis Between Good Feedback Descriptors on Online Courses. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1512-1523.
DOI: https://doi.org/10.5753/sbie.2023.235316.
