The DOI matches its Crossref record; 88 of the 90 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
DEWEY, J. M. (1910) · Cited by 6,801
Information Visualization: Perception for Design
Ware, C. (2000) · doi:10.1016/b978-0-12-381464-7.00018-1 · Cited by 3,742
Visualization Analysis and Design
Munzner, T. (2014) · A K Peters/CRC Press eBooks · doi:10.1201/b17511 · Cited by 1,720
Narrative Visualization: Telling Stories with Data
Segel, E., & Heer, J. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.179 · Cited by 1,334
McDonald, N., Schoenebeck, S., & Forte, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359174 · Cited by 1,206
Learning Analytics: Ethical Issues and Dilemmas
Slade, S., & Prinsloo, P. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479366 · Cited by 1,078
Erickson, T. D., & Kellogg, W. A. (2000) · ACM Transactions on Computer-Human Interaction · doi:10.1145/344949.345004 · Cited by 1,002
Designing Theory-Driven User-Centric Explainable AI
Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906
Anderson, J. R., Boyle, C., & Reiser, B. (1985) · Science · doi:10.1126/science.228.4698.456 · Cited by 699
Flagged first, then by citations
2 of the 90 references couldn’t be matched to a known paper.
Research works printed but not linked: 26
- Paulo Blikstein and Marcelo Worsley. 2018. Multimodal learning analytics and assessment of open-ended artifacts. In Learning Analytics in Education, David Niemi, Roy D. Pea, Bror Saxberg and Richard E. Clark (Eds.). IAP, Charlotte, USA, 89.
- 10.1007/s40593-015-0090--8 · DOI
- 10.1016/S0733--8627(03)00007--5 · DOI
- 10.1016/B978044481862--1.50103--5 · DOI
- 10.1016/S0020--7373(80)80016--2 · DOI
- 10.1037/10903-000 · DOI
- Stephen Few. 2004. Show me the numbers: Designing Tables and Graphs to Enlighten. Analytics Press, Burlingame, CA, USA.
- 10.1186/s41077-0180086--1 · DOI
- Noah Iliinsky and Julie Steele. 2011. Designing data visualizations: representing informational relationships. O'Reilly Media, Inc., Sebastopol, CA, USA.
- 10.1016/B978-0--12--802975--6.00002--8 · DOI
- 10.1097/00024776--200803000-00009 · DOI
- Jong W. Kim, Robert A. Sottilare, Keith Brawner and Timothy Flowers. 2018. Integrating Sensors and Exploiting Sensor Data with GIFT for Improved Learning Analytics. In Proceedings of the Annual GIFT Users Symposium, GIFTSym6, 299--312.
- 10.1002/9781119055259 · DOI · Paper page
- Roberto Martinez-Maldonado, Vanessa Echeverria, Doug Elliott, Carmen Axisa, Tamara Power and Simon Buckingham Shum. 2019. Designing translucent learning analytics with teachers: an elicitation process. Interactive Learning Environments, (in press), 1--30.
- Xavier Ochoa, Katherine Chiluiza, Roger Granda, Gabriel Falcones, James Castells and Bruno Guamán. 2018. Multimodal Transcript of Face-to-Face Group-Work Activity Around Interactive Tabletops. In Proceedings of the CROSS-MMLA workshop on Multimodal Learning Analytics across Spaces, 1--6.
- 10.1007/978--3-030--218140_8 · DOI
- Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos and Sheelagh Carpendale. 2018. Data-driven storytelling. CRC Press, Boca Raton, FL, USA.
- Puripant Ruchikachorn. 2016. Integrating Visualizations into the Authoring of Narratives. Ph.D Dissertation Dissertation State University of New York at Stony Brook NY USA.
- 10.1007/s11412-014--9202-y · DOI
- Hans-Jörg Schulz, Marc Streit, Thorsten May and Christian Tominski. Year. Towards a characterization of guidance in visualization. In Proceedings of the IEEE Conference on Information Visualization (InfoVis'13), 1--2.
- 10.1109/TLT.2016.2599522 · DOI
- 10.1007/s40593017-0146-z · DOI
- Alice Thudt, Jagoda Walny, Theresia Gschwandtner, Jason Dykes and John Stasko. 2018. Exploration and Explanation in Data-Driven Storytelling. In Data-Driven Storytelling, Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos and Sheelagh Carpendale (Eds.). AK Peters/CRC Press, Natick, MA, USA, 59--83.
- 10.1007/9783--319--67925--9 · DOI
- 10.1007/s40593-017-0160--1 · DOI
- 10.1007/978--3--319--266336_3 · DOI
Cited by
Sorted by citations
Items 101–123 of 123
The Hidden Workload: Student Data Work in Multimodal Algorithmic Evaluations
Méndez, G. G., Benjumea, J. H., Eras, L., Domínguez, F., & Wong-Villacrés, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757672 · Cited by 1
Pfeiffer, A., Heinemann, B., Doveren, J., & Schroeder, U. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783957104106-245 · Cited by 1
Mapping the connection between Learning Analytics and Learning Design
Corrin, L., Law, N. W. Y., & Chen, M. (2023) · ASCILITE Publications · doi:10.14742/apubs.2023.480 · Cited by 1
TEACHING NON-TECHNICAL SKILLS IN A DATA ANALYTICS PROGRAM IN HIGHER EDUCATION
(2021) · Issues in Information Systems · doi:10.48009/3_iis_2021_214-229
Fiacco, J., Jiang, S., Adamson, D., & Rosé, C. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.14012-6
Multimodal Learning Analytics for Educational Digital Storytelling
Lambropoulos, N. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-9527-8.ch014
How LLMs Support EFL Writing:A Case Study of K-12 English Learning Based on the EDIPT Model
Dai, Y. M., Panghe, Q., Zhang, Y., Zhang, M., & Xu, X. (2024) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir62538.2024.10959858 · Cited by 1
Closing the Learning Analytics Loop through Explanatory Dashboard DesignPreprint
Klein, S., & Sellberg, C. (2025) · Research Square · doi:10.21203/rs.3.rs-7408850/v1
Hu, R., Huang, T., Yin, X., & Jiang, E. (2025) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2025.2484568
Milesi, M. E., Alfredo, R., Echeverría, V., Yan, L., Zhao, L., Jin, Y., Gašević, D., Tsai, Y., & Martínez‐Maldonado, R. (2026) · Smart Learning Environments · doi:10.1186/s40561-026-00434-4 · Cited by 1
Truong, N., & Hasan, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7462-8.ch003
Expanding a Temporal Vocabulary Towards Designing for “Undiscoverable Learning”
Perez, M., & García, P. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791827
Evaluating AI-Generated Narrative Feedback on Nonverbal Communication in Student Presentations
Pilay, K. C., Carrera-Rivera, A., Ochôa, X., & Córdova-García, J. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785074
Fan, Y., Soon, W. M. A., & Kah Loon, N. G. (2026) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2026.2685694
Constructing Ground Truth for Collaborative Action Recognition: Lessons from Human--VLM Disagreement
Spikol, D., Li, Z., & Yamaguchi, S. (2026) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3774398.3811583
Enhancing situation awareness for disaster policymaking via LLM-generated narratives
Shao, R., & Fan, C. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103441 · Cited by 1
Reimagining Career Counseling in India: Large Language Models as Catalysts for Accessible Guidance
Yashoda, M. B., Revathy, V. R., Jishnu, M. S., Paul, V. G., Sevanth, M., Robinson, A. P., & Chauhan, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20612-1_18
Hocine, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39106-3_3
Buzz, Bid, Body: Multimodal Practices of Distributed Recognition
Puthipiroj, J. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3828960
Towards a Mapping for AI-Based Eye-Tracking Analytics in Multimedia Learning
Tsiakas, K., Zermiani, F., İren, D., Duong‐Trung, N., Raschke, M., Klemke, R., Kravčík, M., Salmerón, L., Jarodzka, H. M., & Catrysse, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37982-5_36
From Eye Movements to Instructional Insights: Co-designing an AI Dashboard with Teachers
Tsiakas, K., Catrysse, L., Buseyne, S., van Daal, T., Donche, V., Gijbels, D., & Jarodzka, H. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37982-5_37
Li, Z., Holm-Janas, V., Yamaguchi, S., & Spikol, D. (2026) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt68022.2026.00056
Mendes, J. L., Albuquerque, G. O., Dantas, D. L., & da Silva, M. M. O. (2026) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2026.28218
Metadata
- 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 Graphics and Computer-Aided Design › 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
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 ›
Are you an author, or do you see a problem with this page? Request a review