Many recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, fosters engagement and motivation, and enables personalized learning experiences. However, the use of feedback processes in real-world everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses, especially in university contexts. The present study aimed to address this challenge in real university classes, laying the groundwork for the future development of an automated intelligent system that can support university teachers in delivering personalized feedback to a large group of students in real-world environments. Specifically, it focused on the prospect of gathering quality data from an actual academic course, intending to appropriately fuel AI-based techniques. These techniques could enhance the comprehension of students’ learning evidence and offer valuable insights to teachers. This paper presents an experimental work, carried out in the academic year 2020/21, which involved 220 students attending the first year of the Master’s Degree course in Primary Education. The components of teachers’ professional vision were explored using a rubric developed by the research team. Preliminary results suggested that the rubric can be effective in capturing sequential information about students’ development of professional vision. Thus, it can be further exploited to collect data from the process and feed AI-based algorithms. Further developments include exploring other machine learning techniques to reduce observers’ bias and support teachers in providing more effective personalized feedback.

Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques / Gratani, F.; Screpanti, L.; Giannandrea, L.; Scaradozzi, D.; Capolla, L. M.. - 2076:(2024), pp. 440-454. (Intervento presentato al convegno 5th International Conference on Higher Education Learning Methodologies and Technologies Online, HELMeTO 2023 tenutosi a Foggia, Italia nel 2023) [10.1007/978-3-031-67351-1_30].

Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques

Gratani F.
;
Screpanti L.
;
Scaradozzi D.;
2024-01-01

Abstract

Many recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, fosters engagement and motivation, and enables personalized learning experiences. However, the use of feedback processes in real-world everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses, especially in university contexts. The present study aimed to address this challenge in real university classes, laying the groundwork for the future development of an automated intelligent system that can support university teachers in delivering personalized feedback to a large group of students in real-world environments. Specifically, it focused on the prospect of gathering quality data from an actual academic course, intending to appropriately fuel AI-based techniques. These techniques could enhance the comprehension of students’ learning evidence and offer valuable insights to teachers. This paper presents an experimental work, carried out in the academic year 2020/21, which involved 220 students attending the first year of the Master’s Degree course in Primary Education. The components of teachers’ professional vision were explored using a rubric developed by the research team. Preliminary results suggested that the rubric can be effective in capturing sequential information about students’ development of professional vision. Thus, it can be further exploited to collect data from the process and feed AI-based algorithms. Further developments include exploring other machine learning techniques to reduce observers’ bias and support teachers in providing more effective personalized feedback.
2024
9783031673504
9783031673511
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/337252
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