A feedback model for data-rich learning experiences
Abstract
Feedback has been identified as one of the factors with the largest potential for a positive impact in a learning experience. There is a significant body of knowledge studying feedback and providing guidelines for its implementation in learning environments. In parallel, the areas of learning analytics or educational data mining have emerged to explore how to analyse exhaustive digital trails produced by technology mediation to improve learning experiences. Current conceptualisations of feedback do not take into account the presence of these trails nor the presence of knowledge extracted through analytics techniques. This paper presents a model to reconceptualise feedback in data-rich learning experiences. It acknowledges the presence of algorithms to analyse and predict learner behaviour and proposes an integrated view between these elements as part of the feedback process and aspects conventionally present in previous models. This new conceptualisation offers instructors, designers and researchers a framework to formulate feedback processes in scenarios with comprehensive data capturing, while retaining a solid connection with well-established educational theories.
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It has been suggested that LA should be an essential component of feedback design in data-rich environments.
“and it has been suggested that LA should therefore be an essential component of feedback design in data-rich environments (Er et al., 2021; Pardo, 2018).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Pardo, 2018
Several studies have explored the potential for LA to improve feedback practices since the emergence of the field in 2011.
“Since the emergence of the field of LA in 2011, several studies have explored the potential for LA to improve feedback practices (Misiejuk et al., 2021; Pardo, 2018; 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, 2018
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“The use of learning analytics has also shown its potential to enhance both student retention (Arnold & Pistilli, 2012) and quality of feedback (Lim et al., 2021; Pardo, 2018), and to inform teaching practice (Martínez-Maldonado et al., 2022).”
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“Pardo’s model (Pardo, 2017), mainly grounded in the literature of self-regulated learning (Butler & Winne, 1995), suggests a feedback process where instructors can tailor the content of the feedback based on certain conditions (e.g., answering less than 5 questions in a quiz) that are configured by…”
MethodTempelaar, D. T. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1677855 · Cited by 76 · see citing works
“Learning analytics is synonymous with ‘ data-rich learning experiences ’ (Pardo 2018): since part or all of the learning is taking place in technology-enhanced learning environments, the performance data available in any module are enriched with learning activity data, collected by systematically…”
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“The diversity of uses (and indeed teachers and students and their individual characteristics) also precluded predominantly quantitative measures of impact, even though they may be possible for more focused programs (Dawson, Jovanovic, Ga š evi ć , & Pardo, 2017).”
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Tsai, Y.-S., Mello, R. F., Jovanović, J., & Gašević, D. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448212 · Cited by 41 · see citing works
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“Learning analytics as a field was manifested within this context (Pardo, 2018).”
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“The design of formative feedback that is actionable and important for discussion is a large research questions in itself (Jørnø and Gynther, 2018; Pardo, 2018) and is beyond the scope of this paper; however, analysis framework we present here represents an important first step in this direction for…”
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“Learning through Feedback Loops To better understand the impact of feedback systems on learning, scholars who have written about the importance of feedback, such as John Dewey [25,26], have referred to the importance of analyzing the feedback-loop character of learning as a guiding framework for supporting better lear…”
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“In the setting of an undergraduate engineering course, Pardo et al. describe how an approach that combined instructor knowledge with digital traces of student interaction around technology allowed for the generation of regular and personalized e-mail feedback messages that met with a positive response from learners an…”
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Assessing the sequencing of learning objectives in a study program using evidence-based practice
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“These can include feedback about progress or prompts (in the form of personalised messages; Pardo 2018) to support learners to pay attention to specific course learning objectives in the subsequent courses based on the mastery of skills in the previous course.”
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“Similarly, several studies have shown that LA fails to effectively provide students with actionable knowledge or effective learning strategies, resulting in their apathy to take action based on the feedback given to them (Matcha et al., 2020; Pardo, 2018).”
BackgroundAutomated intelligent feedback in university statistics education
Tacoma, S. (2020) · doi:10.33540/395 · Cited by 10 · see citing works
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“An exciting recommendation was made by Pardo (2017), who argued that new feedback approaches need to consider integrating computer-based and human-produced feedback into a more detailed description, involving elements that capture the interaction between learners, computers, instructors and…”
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“New forms of feedback can thus make use of the trace data in “data-rich” learning (Pardo, 2017).”
BackgroundCheng, G., Chwo, G. S.-M., & Ng, W. S. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.2001539 · Cited by 11 · see citing works
“The use of technology to process large datasets and generate automated analysis is a promising approach, but there has been a dearth of research exploring this kind of technological initiatives (Cheng, 2017a, 2017b; Pardo, 2018).”
MethodExploration of Analytical Mechanisms in the Feedback model
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Establishing Trustworthy Artificial Intelligence in Automated FeedbackPreprint
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Learning Analytics for Peer Assessment: A Scoping Review
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MAATSE: Prototyping and Evaluating an Open and Modular E-Assessment Tool for STEM Education
Rolwes, A., Stellbauer, P., Lungershausen, U., Čubela, D., Böhm, K., & Neis, P. (2023) · IEEE German Education Conference (GECon) · doi:10.1109/gecon58119.2023.10295151 · Cited by 6 · see citing works
“We prototyped MAATSE to create an AA tool, fulfilling the criteria of Pardo [9] and Tacoma et al. [10], and overcoming the obstacles posed by the different LMS and (inter)disciplinary requirements.”
BackgroundMethodPowerful Student-Facing Dashboard Design Through Effective Feedback, Visualization, and Gamification
Bayrak, F., Kibar, P. N., & Kocadere, S. A. (2021) · Advances in analytics for learning and teaching · doi:10.1007/978-3-030-81222-5_7 · Cited by 6 · see citing works
e-FeeD4Mi: human-centred design of personalised and contextualised feedback in MOOCs
Topali, P., Ortega‐Arranz, A., Asensio-Pérez, J. I., Villagrá‐Sobrino, S., Martínez‐Monés, A., & Dimitriadis, Y. A. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2376201 · Cited by 5 · see citing works
“Pardo (2018) suggested a feedback model that enables stakeholders to select data-driven indicators, establish similar if-then conditions, and deliver personalised feedback through email messages to different student cohorts given their course engagement.”
Human-centered Approaches to Data-informed Feedback
Tsai, Y., & Martínez‐Maldonado, R. (2022) · Solar eBooks · doi:10.18608/hla22.021 · Cited by 5 · see citing works
e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments
Ortega‐Arranz, A., Topali, P., Asensio-Pérez, J. I., Villagrá‐Sobrino, S., Martínez‐Monés, A., & Dimitriadis, Y. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16290-9_39 · Cited by 4 · see citing works
A computational model for individual differences in non-reinforced learning for individual itemsPreprint
Salomon, T., Itzkovitch, A., Daw, N. D., & Schönberg, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.20.484477 · Cited by 4 · see citing works
Data-informed nudges for student engagement and successPreprint
Blumenstein, M., Liu, D., Richards, D., Leichtweis, S., & Stephens, J. M. (2018) · doi:10.31234/osf.io/qjmpn · Cited by 4 · see citing works
Hybrid Intelligence Feedback: A Model Proposal and Preliminary Study
Coşkun, A., & Er, E. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70212 · Cited by 3 · see citing works
Desmistificando a adoção de Learning Analytics: um guia conciso sobre ferramentas e instrumentos
Freitas, E., Falcão, T. P., & Mello, R. F. (2020) · doi:10.5753/sbc.5627.6.4 · Cited by 3 · see citing works
UNDERGRADUATE STEM AND NON-STEM STUDENTS’ INTERPRETATION OF MEAN IN AN INFOGRAPHIC
Lužanin, Z., Kaplar, M., & Hrubik-Vulanović, T. (2022) · Journal of Baltic Science Education · doi:10.33225/jbse/22.21.638 · Cited by 3 · see citing works
Are You Feeding Back or Is It Taking Students Forward?
Dann, C. E., & O’Neill, S. (2019) · Advances in higher education and professional development book series · doi:10.4018/978-1-7998-0426-0.ch014 · Cited by 3 · see citing works
Hausman, M., Verpoorten, D., Defaweux, V., & Detroz, P. (2019) · Advances in higher education and professional development book series · doi:10.4018/978-1-7998-1238-8.ch013 · Cited by 3 · see citing works
Suliman, S., Kassab, S. E., Iqbal, M. Z., Al‐Bualy, R., Asoodar, M., Susilo, A. P., de Mortier, C. A., Boussé, M., Hancock, N., Mathew, T., et al. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2581163 · Cited by 2 · see citing works
The impact of online feedback on student learning outcomes: a meta-analysis study
Adıgüzel, O. C., Esen, E., & Karagöl, İ. (2025) · Asia Pacific Education Review · doi:10.1007/s12564-025-10099-2 · Cited by 3 · see citing works
Vigentini, L., Swibel, B., & Hasler, G. (2021) · Advances in analytics for learning and teaching · doi:10.1007/978-3-030-81222-5_14 · Cited by 2 · see citing works
Developing a Growth Learning Data Mindset
Vigentini, L., Swibel, B., & Hasler, G. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.7377 · Cited by 2 · see citing works
“Successful examples of specific interventions expose data to students, elicit a reaction, or provide specific guidance (Arnold & Pistilli, 2012; Lim et al., 2019b; Pardo, 2018).”
BackgroundTeaching strategies of distance teaching applied in design discipline
Wang, C., & Lin, C. P. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2223235 · Cited by 2 · see citing works
“Because feedback is one of the processes that connect individual students to teachers through course content (Pardo, 2018; Rienties et al., 2015).”
Students got mail: Do students read semi-tailored emails and what is the impact?
Reynolds, M., Tyler, J., Wakefield, J., & Wight, R. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8812 · Cited by 3 · see citing works
“With feedback noted as one of the most powerful factors for student learning (Hattie, 2007; Pardo, 2018) and one-size-fits-all approaches to feedback regarded as inappropriate, technological-based interventions facilitating the efficient delivery of tailored feedback at scale appear promising…”
BackgroundMethodInfluentialProblematizing feedback loops: ‘on’, ‘with’, and ‘beyond’ analytics dashboards in MOOCs
van de Oudeweetering, K., Knox, J., & Decuypere, M. (2023) · Learning Media and Technology · doi:10.1080/17439884.2023.2264188 · Cited by 2 · see citing works
“Moreover, these e ff orts have resulted in analytics dashboards for learners that encourage continuous re fl ection and self-regulation (Pardo 2018; Rogers, Dawson, and Ga š evi ć 2021).”
Zhang, S., & Yingsoon, G. Y. (2023) · International Journal of Education & Technology · doi:10.59021/ijetech.v1i1.15 · Cited by 1 · see citing works
“The crucial concept in support of students is feedback, as Pardo (2018) mentioned.”
BackgroundKekurangan Aplikasi Grammarly dalam Mendeteksi Kesalahan pada Karya Tulis Mahasiswa
Tambunsaribu, G., & Galingging, Y. (2023) · DIALEKTIKA JURNAL BAHASA SASTRA DAN BUDAYA · doi:10.33541/dia.v10i1.4838 · Cited by 1 · see citing works
“Hal ini tidak bisa dihindari bahwa para guru akan sangat kewalahan dalam memberikan umpan balik yang efektif kepada para siswanya (Pardo, 2018; Pardo, et al., 2019; Lim, et al., 2021).”
BackgroundInfluentialVillasol, C. D. L. I. (2021) · Digital Education Review · doi:10.1344/der.2021.39.192-212 · Cited by 1 · see citing works
LAFe: Learning Analytics Solutions to Support On-Time Feedback
Mello, R. F., Alves, G., Oliveira, E., Pérez‐Sanagustín, M., Hilliger, I., Villalobos, E., Félix, E., & Broisin, J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64312-5_61 · Cited by 1 · see citing works
AI Technologies in Modern Farming
Kusmiatun, A., Lestyarini, B., Rakhmawati, A., & Saddhono, K. (2024) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat62666.2024.10923283 · Cited by 1 · see citing works
Okpalauwaekwe, U., Polreis, S., & D’Eon, M. F. (2021) · Canadian Medical Education Journal · doi:10.36834/cmej.71595 · Cited by 1 · see citing works
Technologies, Teaching, and Learning in Higher Education
Lodge, J. M. (2019) · doi:10.1093/obo/9780199756810-0221 · Cited by 1 · see citing works
Chen, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4589867 · Cited by 1 · see citing works
Thompson, W. (2019) · Queensland University of Technology · doi:10.5204/thesis.eprints.134169
The Bi-directional Effect Between Data and Assessments in the Digital Age
Pardo, A., & Reimann, P. (2020) · The enabling power of assessment · doi:10.1007/978-3-030-41956-1_12
Contemporary Teacher Education: A Global Perspective
Tirri, K. A. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-1689-9
Suyan, Z., & Yingsoon, G. Y. (2023) · International Conference on University Teaching and Learning (InCULT) · doi:10.1109/incult59088.2023.10482626
“Blended learning environments that use technology to provide adaptive and personalized learning experiences have shown to be particularly effective in increasing student engagement [27].”
BackgroundAssessment Analytics: Feedback, Feedup, Feedforward on Bayesian Network
Müftüoğlu, C. T., Oral, Ö., & Yurdugül, H. (2024) · Advances in analytics for learning and teaching · doi:10.1007/978-3-031-56365-2_14
Bezen, S., & Derman, İ. (2026) · Educational Psychology · doi:10.1080/01443410.2026.2680187
- Authors
- Pardo, A.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Pardo, Abelardo
- ORCID
- Pardo, Abelardo
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2017-07-20
- Volume
- 43
- Issue
- 3
- Pages
- 428-438
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W2736743378
- Cited by
- 82
- References
- 46
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 14 · 2022: 12 · 2021: 17 · 2020: 11 · 2019: 13 · 2018: 4
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- 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
- feedback, learning analytics, educational data mining, feedback model, learner data, educational technology
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