The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course
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“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 [35]
In the citing paper
“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 [135]
In the citing paper
“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 [135]
Citations
Cites
Discovering Statistics Using SPSS
Field, A. P., & Miles, J. N. V. (2000) · Medical Entomology and Zoology · Cited by 28,269
(2002) · Cited by 13,211
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Nicol, D. J., & Macfarlane‐Dick, D. (2006) · Studies in Higher Education · doi:10.1080/03075070600572090 · Cited by 5,846
Feedback and Self-Regulated Learning: A Theoretical Synthesis
Butler, D. L., & Winne, P. H. (1995) · Review of Educational Research · doi:10.3102/00346543065003245 · Cited by 3,334
Discovering statistics using R
(2012) · Choice Reviews Online · doi:10.5860/choice.50-2114 · Cited by 2,548
Metacognition in Educational Theory and Practice
Hacker, D. J., Dunlosky, J. T., & Graesser, A. C. (1998) · Routledge eBooks · doi:10.4324/9781410602350 · Cited by 2,138
Studying as Self-Regulated Learning
(1998) · Metacognition in Educational Theory and Practice · doi:10.4324/9781410602350-19 · Cited by 1,589
Let’s not forget: Learning analytics are about learning
Gašević, D., Dawson, S., & Siemens, G. (2015) · TechTrends · doi:10.1007/s11528-014-0822-x · Cited by 1,049
Self-citationWinstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017) · Educational Psychologist · doi:10.1080/00461520.2016.1207538 · Cited by 884
McKenzie, K., & Schweitzer, R. (2001) · Higher Education Research & Development · doi:10.1080/07924360120043621 · Cited by 799
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.10.002 · Cited by 767
Self-citationBlikstein, P., & Worsley, M. B. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.32.11 · Cited by 539
Using learning analytics to scale the provision of personalised feedback
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12592 · Cited by 425
Self-citationClow, D. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330636 · Cited by 407
Duval, E. (2011) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2090116.2090118 · Cited by 330
Bodily, R., & Verbert, K. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2017.2740172 · Cited by 306
Harks, B., Rakoczy, K., Hattie, J., Besser, M., & Klieme, E. (2013) · Educational Psychology · doi:10.1080/01443410.2013.785384 · Cited by 287
Designing pedagogical interventions to support student use of learning analytics
Wise, A. F. (2014) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2567574.2567588 · Cited by 284
Duffy, M. C., & Azevedo, R. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.05.041 · Cited by 251
Hermsen, S., Frost, J., Renes, R. J., & Kerkhof, P. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.023 · Cited by 194
Susan Bull, A., & Kay, J. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0090-8 · Cited by 180
Research Evidence on the Use of Learning Analytics: Implications for Education Policy
Ferguson, R., Andrew Brasher, A., Clow, D., Adam, C., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Riina, V. (2016) · Research Explorer (The University of Manchester) · doi:10.2791/955210 · Cited by 179
Modeling academic achievement by self-reported versus traced goal orientation
Zhou, M., & Winne, P. H. (2012) · Learning and Instruction · doi:10.1016/j.learninstruc.2012.03.004 · Cited by 172
Winne, P. H., & Baker, R. S. (2013) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554619 · Cited by 165
How Software Technologies Can Improve Research on Learning and Bolster School Reform
Winne, P. H. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4101_3 · Cited by 158
Student Facing Dashboards: One Size Fits All?
Teasley, S. D. (2017) · Technology Knowledge and Learning · doi:10.1007/s10758-017-9314-3 · Cited by 143
Multimodal data to design visual learning analytics for understanding regulation of learning
Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.019 · Cited by 140
OnTask: Delivering Data-Informed, Personalized Learning Support Actions
Pardo, A., Bartimote-Aufflick, K., Shum, S. B., Dawson, S., Gao, J. P., Gašević, D., Leichtweis, S., Liu, D., Martínez‐Maldonado, R., Mirriahi, N., et al. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.53.15 · Cited by 140
Self-citationData-Driven Personalization of Student Learning Support in Higher Education
Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_5 · Cited by 128
Self-citationColvin, C., Rogers, T., Wade, A. T., Dawson, S., Gašević, D., Shum, S. B., Nelson, K. J., Alexander, S., Lockyer, L., Kennedy, G. E., et al. (2015) · UTS ePRESS (University of Technology Sydney) · Cited by 116
Self-citationKrumm, A. E., Waddington, R. J., Teasley, S. D., & Lonn, S. (2014) · Springer eBooks · doi:10.1007/978-1-4614-3305-7_6 · Cited by 114
Issues in researching self-regulated learning as patterns of events
Winne, P. H. (2014) · Metacognition and Learning · doi:10.1007/s11409-014-9113-3 · Cited by 109
Sonnenberg, C., & Bannert, M. (2015) · Journal of Learning Analytics · doi:10.18608/jla.2015.21.5 · Cited by 108
A feedback model for data-rich learning experiences
Pardo, A. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1356905 · Cited by 82
Self-citationLust, G., Elen, J., & Clarebout, G. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.03.014 · Cited by 73
Dawson, S., Jovanović, J., Gašević, D., & Pardo, A. (2017) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3027385.3027405 · Cited by 66
Self-citationProceedings of the Fourth International Conference on Learning Analytics And Knowledge
Pistilli, M., USA, L. '. L. A. A. K. C. 2. 2. I. I., & Association for Computing Machinery-Digital Library (2014) · doi:10.1145/2567574 · Cited by 65
Effects of instructional conditions and experience on the adoption of a learning tool
Gašević, D., Mirriahi, N., Dawson, S., & Joksimovíc, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.026 · Cited by 60
Self-citationTsai, Y., Moreno-Marcos, P. M., Tammets, K., Kollom, K., & Gašević, D. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170367 · Cited by 55
Self-citationBeheshitha, S. S., Gašević, D., & Hatala, M. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723628 · Cited by 43
Self-citationO'Connell, S. D., & Lang, G. (2018) · Journal of Research on Technology in Education · doi:10.1080/15391523.2017.1408438 · Cited by 40
Dodge, B., Whitmer, J., & Frazee, J. P. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723657 · Cited by 26
Provision of Data-Driven Student Feedback in LA & EDM
Pardo, A., Poquet, O., Martínez‐Maldonado, R., & Dawson, S. (2017) · Society for Learning Analytics Research (SoLAR) eBooks · doi:10.18608/hla17.014 · Cited by 25
Self-citationAn Approach for an Affective Educational Recommendation Model
Santos, O. C., Boticario, J. G., & Riesco, Á. M. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0530-0_6 · Cited by 24
Evaluation of a learning analytics application for open edX platform
Ruipérez‐Valiente, J. A., Muñoz‐Merino, P. J., Pijeira, D., Santofimia, R., & Kloos, C. D. (2016) · Computer Science and Information Systems · doi:10.2298/csis160331043r · Cited by 21
Engagement in Social Learning: Detecting Engagement in Online Communities of Practice
Pesare, E., Roselli, T., & Rossano, V. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-42070-7_15 · Cited by 14
Mean and Variance Modeling of Under- and Overdispersed Count Data
Smith, D. M., & Faddy, M. J. (2016) · Journal of Statistical Software · doi:10.18637/jss.v069.i06 · Cited by 13
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Promises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499
Self-citationAutomatic feedback in online learning environments: A systematic literature review
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100027 · Cited by 440
Self-citationBanihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100489 · Cited by 246
Molenaar, I., Mooij, S. D., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107540 · Cited by 217
Self-citationEffects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198
“…by process analyses, as theorized by SRL models (e.g., Zimmerman, 2002), such as first analyzing the task, monitoring while performing various cognitive activities, and evaluating learning (Bannert, Reimann, & Sonnenberg, 2014; Lim et al., 2021; Paans, Molenaar, Segers, & Verhoeven, 2019).”
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Metadata
- 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
- Venue
- Learning and Instruction
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 72
- Pages
- 101202
- ISSN
- 0959-4752 (ISSN-L)
- 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
- 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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