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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

  1. Cavalcanti, A.P., et al.: Automatic feedback in online learning environments: a systematic literature review. Comput. Educ. Artif. Intell. 2, 100027 (2021)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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

    Chapter  Google Scholar 

  4. Falcão, T.P., et al.: Tutoria: a software platform to improve feedback in education. J. Interact. Syst. 14(1), 383–393 (2023)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. 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

    Google Scholar 

  7. 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)

    Google Scholar 

  8. Hattie, J., Timperley, H.: The power of feedback-review of educational research. American Education Research Association and SAGE, p. 86 (2011)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. Jørnø, R.L., Gynther, K.: What constitutes an ‘actionable insight’ in learning analytics? J. Learn. Anal. 5(3), 198–221 (2018)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Pardo, A.: A feedback model for data-rich learning experiences. Assess. Eval. High. Educ. 43(3), 428–438 (2018)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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

    Chapter  Google Scholar 

  18. Spencer, D.: Card sorting: Designing usable categories. Rosenfeld Media (2009)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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

    Chapter  Google Scholar 

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Correspondence to Rafael Ferreira Mello , Gabriel Alves , Elaine Harada , Mar Pérez-Sanagustín or Isabel Hilliger .

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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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  • DOI: https://doi.org/10.1007/978-3-031-64312-5_61

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