Abstract
Feedback given to students by instructors is essential to guide students and help them improve from their mistakes. However, in higher education, instructors feel unable to give quality and timely feedback due to work overload and lack of time. In this context, this tutorial intends to discuss possible data-based and AI solutions for supporting students and instructors in the feedback process. It will include: a panel discussion about the importance of automating the feedback process, a demo of tools for this goal, and a card sorting activity to understand important aspects of developing tools to support on-time feedback.
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References
Cavalcanti, A.P., et al.: Automatic feedback in online learning environments: a systematic literature review. Comput. Educ. Artif. Intell. 2, 100027 (2021)
Cavalcanti, A.P., et al.: How good is my feedback? a content analysis of written feedback. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 428–437 (2020)
Donoso-Guzmán, I., Ooge, J., Parra, D., Verbert, K.: Towards a comprehensive human-centred evaluation framework for explainable AI. In: Longo, L. (ed.) xAI 2023. CCIS, vol. 1903, pp. 183–204. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-44070-0_10
Falcão, T.P., et al.: Tutoria: a software platform to improve feedback in education. J. Interact. Syst. 14(1), 383–393 (2023)
Falcao, T.P., et al.: Tutoria: supporting good practices for providing written educational feedback. In: Anais do XXXIII Simpósio Brasileiro de Informática na Educação, pp. 668–679. SBC (2022)
Félix, E., Oliveira, E.H.T., Ramos, I.M.M., Pérez-Sanagustín, M., Villalobos, E., Hilliger, I., Ferreira Mello, R., Broisin, J.: Designing actionable and interpretable analytics indicators towards explainable AI-based feedback. In: European Conference on Technology Enhanced Learning. Springer (2024). Submitted
Garcia, S., Marques, E., Mello, R.F., Gašević, D., Falcão, T.P.: Aligning expectations about the adoption of learning analytics in a Brazilian higher education institution. In: Proceedings of the Conference of Artificial Intelligence in Education, pp. 1–6 (2021)
Hattie, J., Timperley, H.: The power of feedback-review of educational research. American Education Research Association and SAGE, p. 86 (2011)
Hilliger, I., Celis, S., Perez-Sanagustin, M.: Engaged versus disengaged teaching staff: a case study of continuous curriculum improvement in higher education. High Educ. Pol. 35(1), 81–101 (2022)
Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: preparing learning analytics dashboards for educational practice. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 31–40 (2018)
Jørnø, R.L., Gynther, K.: What constitutes an ‘actionable insight’ in learning analytics? J. Learn. Anal. 5(3), 198–221 (2018)
Krusche, S., Seitz, A.: Artemis: an automatic assessment management system for interactive learning. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 284–289 (2018)
Marin, V.J., Pereira, T., Sridharan, S., Rivero, C.R.: Automated personalized feedback in introductory java programming MOOCs. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 1259–1270. IEEE (2017)
Pardo, A.: A feedback model for data-rich learning experiences. Assess. Eval. High. Educ. 43(3), 428–438 (2018)
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., Mirriahi, N.: Using learning analytics to scale the provision of personalised feedback. Br. J. Edu. Technol. 50(1), 128–138 (2019)
Pérez-Álvarez, R.A., Maldonado-Mahauad, J., Sharma, K., Sapunar-Opazo, D., Pérez-Sanagustín, M.: Characterizing learners’ engagement in MOOCs: an observational case study using the NoteMyProgress tool for supporting self-regulation. IEEE Trans. Learn. Technol. 13(4), 676–688 (2020)
Pérez-Sanagustín, M., Pérez-Álvarez, R., Maldonado-Mahauad, J., Villalobos, E., Sanza, C.: Designing a moodle plugin for promoting learners’ self-regulated learning in blended learning. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds.) EC-TEL 2022. LNCS, vol. 13450, pp. 324–339. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16290-9_24
Spencer, D.: Card sorting: Designing usable categories. Rosenfeld Media (2009)
Tsai, Y.S., Mello, R.F., Jovanović, J., Gašević, D.: Student appreciation of data-driven feedback: A pilot study on OnTask. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 511–517 (2021)
Villalobos, E., Hilliger, I., Pérez-Sanagustín, M., González, C., Celis, S., Broisin, J.: Analyzing learners’ perception of indicators in student-facing analytics: a card sorting approach. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds.) EC-TEL 2023. LNCS, vol. 14200, pp. 430–445. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42682-7_29
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Mello, R.F. et al. (2024). LAFe: Learning Analytics Solutions to Support On-Time Feedback. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_61
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