Using learning analytics to scale the provision of personalised feedback
Open accessAbstract
There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years ( N 2013 = 290, N 2014 = 316 and N 2015 = 415).
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GenAI has the potential to provide timely, specific, and constructive feedback.
“Another promise of GenAI in supporting human learning is its potential to provide timely, specific, and constructive feedback, a key element of high-quality instruction and essential for effective learning [34][35][36].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [34]
The Introduction (p. 3) and Conclusions (p. 11) support the potential for learning analytics to help provide timely, personalised feedback, and the Approach (p. 6) describes detailed suggestions sent to students. The paper does not address GenAI, so it does not establish that GenAI can provide the feedback described in the citing sentence. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAnalyzing feedback data helped instructors pinpoint gaps in students' procedural skills.
“Studies [135,136] demonstrated how analyzing feedback data helped instructors pinpoint gaps in students' procedural skills.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [136]
Identification of gaps in procedural skills led to more targeted interventions and improved learning outcomes.
“This identification led to more targeted interventions and improved learning outcomes, ensuring that students received the support they needed to address specific areas of difficulty.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [136]
In online learning environments or with learning technologies, learning analytics offer the potential for provision of personalized, real-time, and relevant feedback at scale.
“Where learners are engaged in online learning environments, however, or making regular use of learning technologies, learning analytics (LA) offer the potential for provision of personalized, real-time, and relevant feedback at scale (Er et al., 2021; Pardo et al., 2019; Ryan et al., 2019; Lim 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 Pardo et al., 2019
Feedback based on LA provides students with personalized feedback given their learning activities.
“provides them with personalized feedback given their learning activities (e.g. Pardo et al., 2019)”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Pardo et al., 2019
Feedback based on LA is positively correlated with educators' time management strategies and satisfaction.
“Selected studies also suggested that feedback based on LA is positively correlated with educators ' time management strategies and satisfaction (e.g. Iraj et al., 2020; Pardo et al., 2019; Shibani et al., 2020).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Pardo et al., 2019
LA feedback for students is mostly focused on their own learning performance to provide personalized feedback and to help them with regulating their learning.
“while LA feedback for students is mostly focused on their own learning performance to provide personalized feedback and to help them with regulating their learning (e.g., Pardo et al., 2019; Sedrakyan et al., 2020).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Pardo et al., 2019
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References 36 · Cited by 425
- Authors
- Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N.
- Affiliations
- The University of Sydney (AU)
- University of South Australia (AU)
- Corresponding author
- Pardo, Abelardo
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2017-11-06
- Volume
- 50
- Issue
- 1
- Pages
- 128-138
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W2768077796
- Cited by
- 425
- References
- 36
- Citations by year
- 2026: 43 · 2025: 63 · 2024: 73 · 2023: 61 · 2022: 55 · 2021: 48 · 2020: 45 · 2019: 27 · 2018: 9 · 2017: 1
- FWCI
- 26.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal of Educational Technology (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Repository of the University of Belgrade Faculty of Organizational Sciences (RFOS) (submittedVersion, other-oa)
- HAL (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- 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, personalized feedback, instructor workload, digital traces, feedback quality, student perceptions, academic performance, blended learning, first-year engineering students, computer systems course
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