From Data to Insights: A Layered Storytelling Approach for Multimodal Learning Analytics
Abstract
Significant progress to integrate and analyse multimodal data has been carried out in the last years. Yet, little research has tackled the challenge of visualising and supporting the sensemaking of multimodal data to inform teaching and learning. It is naïve to expect that simply by rendering multiple data streams visually, a teacher or learner will be able to make sense of them. This paper introduces an approach to unravel the complexity of multimodal data by organising it into meaningful layers that explain critical insights to teachers and students. The approach is illustrated through the design of two data storytelling prototypes in the context of nursing simulation. Two authentic studies with educators and students identified the potential of the approach to create learning analytics interfaces that communicate insights on team performance, as well as concerns in terms of accountability and automated insights discovery.
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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 Martinez-Maldonado et al., 2020
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The role of indoor positioning analytics in assessment of simulation‐based learning
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Learning Analytics in the Era of Large Language Models
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Human-Centered Design of a Dashboard on Students’ Revisions During Writing
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Privacy-Driven Learning Analytics
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A learning analytics dashboard to support students' reflection on collaboration
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Self-citationVisual Analysis of Hyperproperties for Understanding Model Checking Results
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From Sensor Data to Educational Insights
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NetworkNarratives: Data Tours for Visual Network Exploration and Analysis
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Visualizing and Reporting Educational Data with R
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The Scope of Multimodal Learning Analytics in K–8
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Learner models: design, components, structure, and modelling
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A Closer Look at Instructor Use and Sensemaking Processes of Analytics Dashboards
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CPVis: Evidence-based Multimodal Learning Analytics for Evaluation in Collaborative Programming
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Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization
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Discovering Generative Uncertainty in Learning Analytics Dashboards
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Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:
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Human-centered Approaches to Data-informed Feedback
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Edulyze: Learning Analytics for Real-World Classrooms at Scale
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Towards designing a flexible multimodal learning analytics system
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Design practices in visualization driven data exploration for non-expert audiences
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Multimodal learning analytics: a bibliometric and ontological analysis
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Enhancing Collaboration and Performance among EMS Students through Multimodal Learning Analytics
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From Questions to Impact: Unveiling Teachers' Power to Support Actionable Learning Analytics Design
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So What? Unpacking the Complexities in Collaborative Problem Solving with AI-Augmented Sense-Making
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- Authors
- Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., & Buckingham Shum, S.
- Affiliations
- Monash University, Melbourne, VIC, Australia
- Escuela Superior Politécnica del Litoral & University of Technology Sydney, Sydney, NSW, Australia
- University of Technology Sydney, Sydney, NSW, Australia
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-15
- OpenAlex
- W3029417713
- Cited by
- 123
- References
- 90
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 26 · 2023: 17 · 2022: 19 · 2021: 16 · 2020: 4
- FWCI
- 16.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- multimodal learning analytics, data storytelling, sensemaking, nursing simulation, team performance, accountability, teaching and learning support
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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.
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