Cited by 159Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

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What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course

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Lim, Lisa-Angelique, Gentili, Sheridan, Pardo, Abelardo, Kovanović, Vitomir, Whitelock-Wainwright, Alexander, Gašević, Dragan, Dawson, Shane·2021·Learning and Instruction

DOI 10.1016/j.learninstruc.2019.04.003

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“In other cases, some researchers used focus groups (Balavijendran & Burnie, 2018; Lim et al., 2021; Lim et al., 2020), observations of facial expressions (Bouchet et al.”
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“These findings are consistent with those reported by The findings relating to the provision of LA-based feedback are consistent with those reported by Lim et al. (2021); that is, using the LA-based feedback in a course that implemented the BL setting can improve students’ learning achievements.”
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Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: an exploratory study of four courses
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Background
Learning Analytics in Higher Education
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“However, there are several studies that have provided such insight at the course level with very encouraging results, as shown by lim et al. (2021b). A further limitation of the study was that the tool was available for instructor use on a course-by-course basis, preventing us from examining the impact of its use at t…”
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“[35] explored the impact of LA-based automatic feedback on student self-regulation and performance in a large undergraduate course.”
Background
Generating actionable predictions regarding MOOC learners’ engagement in peer reviews
Er, E., Gómez-Sánchez, E., Bote-Lorenzo, M., Dimitriadis, Y., & Asensio-Pérez, J. (2019) · Behavior and Information Technology · doi:10.1080/0144929x.2019.1669222 · Cited by 12
Categorizing learning analytics models according to their goals and identifying their relevant components: A review of the learning analytics literature from 2011 to 2019
Quadir, B., Chang, M., & Yang, J. C. (2021) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2021.100034 · Cited by 9
To Opt in or to Opt Out? Predicting Student Preference for Learning Analytics-Based Formative Feedback
Merikko, J., Ng, K., Saqr, M., & Ihantola, P. (2022) · IEEE Access · doi:10.1109/access.2022.3207274 · Cited by 9
“Receiving personalized feedback with this approach has 73 been associated with a positive impact on student percep74 tion of feedback quality [10] and higher academic achieve75 ment [12].”
Background
Exploring the impact of chat-based collaborative activities and SRL-focused interventions on students’ self-regulation profiles, participation in collaborative activities, retention, and learning in MOOCs
Psathas, G., Tegos, S. D., Demetriadis, S., & Tsiatsos, Τ. (2023) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09394-0 · Cited by 8
“According to Lim et al. (2019), individualized support based on LA leads to better learning outcomes.”
Background
Technology-enhanced Learning and Learning Analytics for personalized STEM learning: A scoping review
Qushem, U. B., Christopoulos, A., Kaliisa, R., Khalil, M., Salakoski, T., & Laakso, M. (2025) · International Journal of Educational Research · doi:10.1016/j.ijer.2025.102827 · Cited by 16
Enhancing academic performance and student success through learning analytics-based personalised feedback emails in first-year chemistry
Kyne, S. H., Lee, M. M. H., & Reyes, C. T. (2023) · Chemistry Education Research and Practice · doi:10.1039/d3rp00032j · Cited by 11
SECA: A Feedback Rules Model in a Ubiquitous Microlearning Context
Tabares, M. S., Vallejo, P., Montoya, A., Sanchez, J., & Correa, D. (2021) · International Conference on Data Science, E-Learning and Information Systems (DATA) · doi:10.1145/3460620.3460745 · Cited by 8
“[Lim et al. 2019], “a challenge for contemporary educators is how to provide feedback to all students in large courses in a personalised, timely and instructive manner.”
Background
Prediction of student performance in blended learning by utilizing learning analytics data
Ozerova, G. P., & Федоровна, П. Г. (2019) · Science for Education Today · doi:10.15293/2658-6762.1906.05 · Cited by 8
Establishing Trustworthy Artificial Intelligence in Automated FeedbackPreprint
Lin, J., Sha, L., Li, Y., Gašević, D., & Chen, G. (2022) · EdArXiv (OSF Preprints) · doi:10.35542/osf.io/5efxn · Cited by 10
Self-citation
Effectiveness of a gamified learning analytics dashboard with coregulation mechanism for self-regulated learning in college ethics courses
Hu, Y.-H., Liao, B.-K., & Hsieh, C.-L. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2277741 · Cited by 11
“LADs serve as e ﬀ ective learning sca ﬀ olds for learners (Lim et al., 2021) and have been designed to support SRL (Matcha et al., 2020; Siemens & Long, 2011).”
EFEKTIVITAS MODEL PEMBELAJARAN DIRECT INSTRUCTION UNTUK MENINGKATKAN KETERAMPILAN MEMBATIK PADA SISWA SEKOLAH DASAR DI SANGGAR BATIK CIKADU
Ma’arif, M. (2020) · JURNAL PAJAR (Pendidikan dan Pengajaran) · doi:10.33578/pjr.v4i1.7894 · Cited by 7
An integrative systematic review analysis of research on technology-enabled personalized learning and self-regulated learning
Halder, D., & Panda, S. (2026) · Smart Learning Environments · doi:10.1186/s40561-025-00428-8 · Cited by 13
“Continuous feedback on readiness, self-efficacy, and persistence sustains autonomy and PL (Sáiz et al., 2020; Lim et al., 2021).”
A Meta-Analysis of the Impact of Feedback Timing on Learning Outcomes in Computer-Assisted Learning
Kandemir, E. N., Esposito, E., Gurgand, L., & Ramus, F. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10117-8 · Cited by 7
Teaching the teachers: A systematic review of genAI-specific technological pedagogical knowledge (TPK) in teacher education
CEALLAIGH, T. Ó., & Murphy, S. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100367 · Cited by 10
Modelling the unique contributions of task understanding to academic challenges and performance
Olivares, L. M. B., Rostampour, R., & Hadwin, A. F. (2024) · Metacognition and Learning · doi:10.1007/s11409-024-09402-0 · Cited by 7
“Similarly, there may be value sampling these task-specific self-reports for multiple study episodes and coupling those with multi-modal traces of studying (Engelmann & Bannert, 2021; Greene, et al., 2021; Järvelä, et al., 2021; Lim, et al., 2021; Taub, et al., 2021).”
The interaction effect of pedagogical agent and emotional feedback on effective learning: a 2 × 2 factorial experiment in online formative assessment
Tang, X., Jiang, L., Liu, G., & Li, H. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1610550 · Cited by 6
An Innovative Framework to Improve Course and Student Outcomes
Alalawi, K., Athauda, R., & Chiong, R. (2021) · International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) · doi:10.1109/citisia53721.2021.9719985 · Cited by 6
Who Can Benefit More From More or Less Elaborated Feedback in a Computer-Based Assessment? Dominant Goal Orientation Matters
Wang, Z., Cao, Y., & Gong, S. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331221132079 · Cited by 6
InPath Forum: A Real-Time Learning Analytics and Performance Ranking Forum System
Yi, A. C. Y., Ying, T. K., Yee, S. J., Wee, M. C., & Tin, T. T. (2022) · IEEE Access · doi:10.1109/access.2022.3227430 · Cited by 6
“[14], [15].”
The design and implementation of a teaching and learning analytics tool in a face-to-face, small-sized course in China’s higher education
Zhang, L., Wu, M., & Ouyang, F. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11940-0 · Cited by 6
“…students’ information and trace data, LA tools have been widely applied to help students make reflection, promote students to increase awareness of the learning process, and support instructors to provide learning guidance (Lim et al., 2019; Papamitsiou and Economides, 2015; Pardo et al., 2018).”
Powerful 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
‘Made good connections’: Amplifying teacher presence and belonging at scale through learning design and personalised feedback
Lim, L.-A., Atif, A., & Farmer, I. W. (2022) · ASCILITE Publications · doi:10.14742/apubs.2022.55 · Cited by 6
“Emerging research in learning analytics feedback interventions has shown benefits for students’ self-regulated learning (Lim et al., 2020) and performance (Lim et al., 2021) in blended learning settings.”
BackgroundMethodSelf-citation
Different modality, different design, different results: Exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activitiesPreprint
Zhou, Q., Suraworachet, W., & Cukurova, M. (2021) · doi:10.35542/osf.io/u3g4n · Cited by 6
“Previous research has reinforced the usual higher predictive accuracy of models generated from multimodal compared to unimodal data [19-21] SRL research with MMLA has been introduced in many contexts such as individual distance learning [22-24] and collaborative learning [25-28].”
Result
Investigating Student and Teacher Perceptions in e-Learning with Learning Analytics and Ontologies
Costa, L., da Silveira, A. C., Souza, M., Salvador, L. N., & Santos, C. A. S. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i08.32411 · Cited by 5
Human-centered Approaches to Data-informed Feedback
Tsai, Y., & Martínez‐Maldonado, R. (2022) · Solar eBooks · doi:10.18608/hla22.021 · Cited by 5
Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy
Fries, A., Pirotte, M., Vanhee, L., Bonnet, P., Quatresooz, P., Debruyne, C., Marée, R., & Defaweux, V. (2023) · Anatomical Sciences Education · doi:10.1002/ase.2346 · Cited by 5
“Learning analytics can produce and transmit information about students' progress as learners, enabling the teacher to regulate and improve existing pedagogical practices (Lim et al., 2021).”
Background
Using Institutional Data to Drive Quality, Improvement, and Innovation
Dart, S., & Cunningham, S. (2023) · University development and administration · doi:10.1007/978-981-19-9438-8_29-1 · Cited by 5
Feedback Assisted by Technology: A Systematic Review of Empirical Research
Huang, W., Stephens, J. M., & Brown, G. (2025) · International Journal of Technology in Education · doi:10.46328/ijte.1061 · Cited by 7
A meta-analysis on the effect of learning analytics interventions on students’ academic performance
Kokoç, M., Bütüner, S. Ö., & Güler, M. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2536571 · Cited by 7
The paradigm wars rage: the need for an integrated approach for the future of higher education
Dollinger, M. (2023) · Advancing Scholarship and Research in Higher Education · doi:10.59197/asrhe.v4i1.8117 · Cited by 3
Constructing Guiding Principles for the Framework of Culturally Responsive Pedagogy-guided Personal Learning Environments in Higher Education
Sun, Y., Xu, X., & Zhang, Y. (2023) · International Conference on Education Technology and Computer · doi:10.1145/3629296.3629307 · Cited by 4
You’ve Got Mail: A Technology-Mediated Feedback Strategy to Support Self-Regulated Learning in First-Year University Students
Sauchelli, I. B., Heath, G., Richardson, A., Lewis, S., & Lim, L.-A. (2023) · Student Success · doi:10.5204/ssj.2825 · Cited by 4
“Lim et al. (2021) demonstrated positive changes in SRL when technology-mediated feedback was introduced into a large first-year course.”
BackgroundMethodResultInfluentialSelf-citation
Supporting And Humanising Behavioural Change without the Behaviourism: Digital Footprints, Learning Analytics and Nudges
Payne, A. L., Compton, M., & Kennedy, S. (2023) · Postdigital science and education · doi:10.1007/978-3-031-31875-7_7 · Cited by 4
Supporting self-regulated learning with learning analytics interventions – a systematic literature reviewPreprint
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1738740/v1 · Cited by 4

Authors: Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S.
Affiliations: University of South Australia (AU)
Monash University (AU)
Corresponding author: Lim, Lisa-Angelique
ORCID: Lim, Lisa-Angelique
Gentili, Sheridan
Pardo, Abelardo
Kovanović, Vitomir
Gašević, Dragan
Dawson, Shane
Venue: Learning and Instruction
Publisher: Elsevier BV
Published: 2021-04
Volume: 72
Pages: 101202
ISSN: 0959-4752 (ISSN-L)
DOI: 10.1016/j.learninstruc.2019.04.003
OpenAlex: W2945685654
Cited by: 159
References: 52
Citations by year: 2026: 16 · 2025: 20 · 2024: 26 · 2023: 32 · 2022: 27 · 2021: 18
FWCI: 17.64
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Learning and Instruction (publishedVersion)
Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
UniSA Research Outputs Repository (University of South Australia) (submittedVersion)
HAL (submittedVersion)
Funders: Office for Learning and Teaching
Topics: Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords: learning analytics, process feedback, student self-regulation, academic performance, personalized feedback, prior academic achievement, e-book usage

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What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course

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