Student appreciation of data-driven feedback: A pilot study on OnTask
Abstract
Feedback plays a crucial role in student learning. Learning analytics (LA) has demonstrated potential in addressing prominent challenges with feedback practice, such as enabling timely feedback based on insights obtained from large data sets. However, there is insufficient research looking into relations between student expectations of feedback and their experience with LA-based feedback. This paper presents a pilot study that examined students’ experience of LA-based feedback, offered with the OnTask system, taking into consideration the factors of students’self-efficacy and self-regulation skills. Two surveys were carried out at a Brazilian university, and the results highlighted important implications for LA-based feedback practice, including leveraging the ‘partnership’ between the human teacher and the computer, and developing feedback literacy among learners.
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Automated LA-based systems, such as OnTask, have demonstrated potential in enhancing self-regulation of learning through personalised feedback interventions.
“Automated LA-based systems, such as OnTask, have demonstrated potential in enhancing self-regulation of learning through personalised feedback interventions (Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Tsai et al., 2021
Empirical studies indicate that feedback informed by LA can enhance self-regulation by offering timely and actionable insights.
“Empirical studies indicate that feedback informed by LA can enhance self-regulation by offering timely and actionable insights (Chen, 2024; Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Tsai et al., 2021
LA-informed, automated feedback with different tools can positively influence self-regulation of learning development.
“it is consistent with previous research suggesting that LAinformed, automated feedback with different tools can positively influence self-regulation of learning development (Chen, 2024; Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Tsai et al., 2021
LA-based feedback services encourage students to trust more in their confidence and self-efficacy.
“mentioned LA-based feedback services not only encourage students to trust more in their confidence and self-efficacy (e.g. Tsai et al., 2021)”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Tsai et al., 2021
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- Authors
- Tsai, Y.-S., Mello, R. F., Jovanović, J., & Gašević, D.
- Affiliations
- Monash University (AU)
- Universidade Federal Rural de Pernambuco (BR)
- University of Belgrade (RS)
- Venue
- LAK21: 11th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2021-04-12
- Pages
- 511-517
- OpenAlex
- W3139953961
- Cited by
- 41
- References
- 21
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 6 · 2022: 13 · 2021: 2
- FWCI
- 9.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- Funders
- European Commission and Erasmus+ (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP)
- CONFAP?CNPq and The UK Academies (APQ-0054-1.03/19)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- learning analytics, data-driven feedback, self-efficacy, self-regulation, feedback literacy, student experience, higher education
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