Multimodal data to design visual learning analytics for understanding regulation of learning
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The proactive GenAI agent is particularly useful for guiding students through complex VLA, especially when integrating insights from multiple sources is required.
“It is particularly useful for guiding students through complex VLA, especially when integrating insights from multiple sources is required (Martinez-Maldonado et al., 2020; Noroozi et al., 2019).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Noroozi et al., 2019
The Implications and conclusions section says SLAM-KIT brings diverse data sources into a unified view, supporting the point about integrating multiple sources in visual learning analytics. But it says the tool is currently used by researchers and that targeting learners is future work; it does not describe a proactive GenAI agent guiding students. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe current study focuses on improved effectiveness and efficiency in comprehending insights from visual analytics and does not necessarily equate to enhanced memory retention, learner engagement, or emotional reaction to VLA.
“However, the current study focuses on improved effectiveness and efficiency in comprehending insights from visual analytics and does not necessarily equate to enhanced memory retention, learner engagement, or emotional reaction to VLA (Noroozi et al., 2019; Vieira et al., 2018).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Noroozi et al., 2019
Section 3 describes making multimodal learning data accessible visually and aims to help learners learn more effectively and efficiently. It does not address whether better comprehension of visual analytics translates into memory retention, learner engagement, or emotional reactions to VLA. Searches for those outcomes and for effectiveness and efficiency of interpreting visualisations found no passage making that distinction; the cited paper also cannot establish the citing authors’ own study focus. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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“…to a deeper understanding of the underexplored role of self-regulation in the MALL research field (Viberg & Andersson, 2019), further theoretical development of the SRL research area (Noroozi et al., 2019), and, finally, the development of adequate LA support for SRL (Viberg, Khalil, et al., 2020).”
BackgroundSystem design for using multimodal trace data in modeling self-regulated learning
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- Authors
- Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T.
- Affiliations
- Wageningen University & Research (NL)
- University of Oulu (FI)
- Open University of the Netherlands (NL)
- Corresponding author
- Noroozi, Omid
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 100
- Pages
- 298-304
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2903932929
- Cited by
- 140
- References
- 52
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 21 · 2023: 19 · 2022: 15 · 2021: 31 · 2020: 15 · 2019: 7
- FWCI
- 15.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computers in Human Behavior (publishedVersion)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by-nc-nd)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Funders
- Finnish Academy (275440)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- multimodal data, learning regulation, learning analytics, graphical user interface, data visualization, cooperative learning, learning environment, data analysis software
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