Abstract
The provision of personalized and timely feedback can become challenging when shifting from face-to-face to online learning. Feedback is not only about providing support to students, but also about identifying when and which students need what kind of support. Usually, educators carry out such activities manually. However, the manual identification, personalization and provision of feedback might turn unmanageable, especially in large-scale environments. Previous works proposed the use of data-driven tools to automate the feedback provision with the active involvement of human agents in its design. Nevertheless, to the best of our knowledge, these tools do not guide instructors in the process of feedback design and sense-making of the data-driven information. This paper presents e-FeeD4Mi, a web-based tool developed to support instructors in the design and automatic enactment of feedback in multiple virtual learning environments. We developed e-FeeD4Mi following a Design-Based Research approach and its potential for adoption has been evaluated in two evaluation studies.
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Notes
- 1.
For simplicity, we refer to instructors as any person involved in the design and provision of feedback, including instructional designers, teachers and teaching assistants.
- 2.
IMS Global. Learning Tools Interoperability (LTI): https://www.imsglobal.org/activity/learning-tools-interoperability, last access: June, 2022.
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Acknowledgements
This research is partially funded by the Spanish State Research Agency (AEI) together with the European Regional Development Fund, under project grant PID2020-112584RB-C32; and by the European Social Fund and Regional Council of Education of Castile and Leon (E-47-2018-0108488).
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Ortega-Arranz, A., Topali, P., Asensio-Pérez, J.I., Villagrá-Sobrino, S.L., Martínez-Monés, A., Dimitriadis, Y. (2022). e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_39
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