Learning analytics to support teachers during synchronous CSCL: balancing between overview and overload
Open accessAbstract
Learning analytics (LA) are summaries, visualizations, and analyses of student data that could improve learning in multiple ways, for example by supporting teachers. However, not much empirical evidence is available yet concerning the effects of LA on how teachers diagnose student progress and intervene during students’ learning activities. The goal of this paper is to summarize the empirical work that was undertaken recently concerning the effect of various types of LA tools on teacher regulation of collaboration groups of students, and to describe the theoretical mechanisms by which LA tools may support teachers in synchronous, moment-to-moment regulation of computer-supported collaborative learning. The hypothesized mechanisms are that LA tools can 1) aggregate information to a manageable level and thereby lower information load, 2) steer the focus of the teacher’s attention, and 3) increase the teacher’s confidence concerning the diagnosis of the situation. In the final section, the role of teacher goals and beliefs in the use of LA tools is discussed, which is argued should be kept in mind when implementing LA in classroom situations.
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If a tool design primarily makes teachers feel validated in their existing beliefs, even when those beliefs are incorrect, it risks maintaining or even amplifying inequitable patterns of teacher behavior.
“if a tool design has the primary effect of making teachers feel validated in their existing beliefs, even when these beliefs are incorrect, then this design risks maintaining or even amplifying inequitable patterns of teacher behavior”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as van Leeuwen, 2015
Section 2.2 says learning analytics can confirm teachers’ impressions and increase confidence in their diagnoses. Section 4.2 describes tools informing subsequent interventions. The paper does not address validation of incorrect beliefs or the risk of maintaining or amplifying inequitable teacher behavior. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDifferent methods of presenting the same underlying algorithm outputs may interact with teachers' existing decision-making biases in very different directions.
“different methods of presentation may interact with teachers' existing decision-making biases in very different directions”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as van Leeuwen 2015
Section 2.1 describes teacher decision-making as an interplay between information presented by the digital interface and how teachers process it. Section 2.3 reports different effects for different learning-analytics tools. The paper does not establish that different presentations of the same algorithm outputs interact with existing decision-making biases in different directions. (LLM, conf=0.78)
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Simon, J. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_7 · Cited by 4 · see citing works
An open learner model used by teachers to monitor speed reading learners
Ginon, B., Johnson, M. D., Türker, A., et al. (2016) · Das Repository der Padagogischen Hoschule St. Gallen (Padagogischen Hoschule St. Gallen) · doi:10.18747/phsg-coll3/id/651 · Cited by 3 · see citing works
Kooperative Unterrichtsettings: Technologische Tools zur Unterstützung von Lehrkräften
van Leeuwen, A., & Rummel, N. (2019) · Unterrichtswissenschaft · doi:10.1007/s42010-019-00051-w · Cited by 3 · see citing works
Barbosa, G. M. D. O. S., Nunes, J. B. C., & Chaves, J. B. (2019) · Revista Iberoamericana de Educación · doi:10.35362/rie8013469 · Cited by 3 · see citing works
Smart Web Data Analytics Quiz and Tests System
Sidhu, M. S., Lee, C. K., & Ali, Z. (2020) · International Conference on Information Technology and Multimedia (ICIMU) · doi:10.1109/icimu49871.2020.9243384 · Cited by 2 · see citing works
Measurement of Teacher’s Orchestration Load: A Framework and a Case Study on Tool Flexibility
Álvarez, C., Amarasinghe, I., Zurita, G., Hernández‐Leo, D., Hakami, L., & Rojas, L. A. (2025) · IEEE Access · doi:10.1109/access.2025.3531241 · Cited by 2 · see citing works
Perspectives on learning from the learning sciences
Fischer, F., Sommerhoff, D., & Keune, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.14007-2 · Cited by 3 · see citing works
Identifying Tertiary Level Educators’ Needs and Understanding of the Collaboration Process Analytics
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Modelado de un sistema de reinscripciones escalable sobre una nube híbrida usando Learning Analytics
Martínez, I. A. C., & García, F. (2025) · TIES Revista de Tecnología e Innovación en Educación Superior · doi:10.22201/dgtic.26832968e.2025.14.99 · Cited by 1 · see citing works
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Identification of Influence Maximizers in Students’ Social Networks
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Operationalizing Learning Processes Through Learning Analytics
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Lourenço, A. A., & Valente, S. N. (2026) · Cognitive technologies · doi:10.1007/978-981-92-0658-2_1
- Authors
- Van Leeuwen, A.
- Venue
- Journal of Learning Analytics
- Published
- 2015-12-07
- Volume
- 2
- Issue
- 2
- Pages
- 138-162
- ISSN
- 1929-7750 (ISSN-L)
- OpenAlex
- W2200572292
- Semantic Scholar
- 82f4290d3048cad554b684d3c7ce8c64b2533b9d
- Cited by
- 84
- References
- 44
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 13 · 2022: 7 · 2021: 14 · 2020: 12 · 2019: 9 · 2018: 9 · 2017: 4 · 2016: 3 · 2015: 1
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Learning Analytics (publishedVersion, cc-by-nc-nd)
- Cosmopolitan Civil Societies: An Interdisciplinary Journal (University of Technology Sydney) (submittedVersion, cc-by-nc-nd)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- 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, teacher support, information overload, information aggregation, teacher attention, teacher confidence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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