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Cited by 123Online Learning and AnalyticsData Visualization and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

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

Martinez-Maldonado, Roberto, Echeverria, Vanessa, Fernandez Nieto, Gloria, Buckingham Shum, Simon·2020·Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3313831.3376148
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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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2 of the 90 references couldn’t be matched to a known paper.

Research works printed but not linked: 26

  1. 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.
  2. 10.1007/s40593-015-0090--8 · DOI
  3. 10.1016/S0733--8627(03)00007--5 · DOI
  4. 10.1016/B978044481862--1.50103--5 · DOI
  5. 10.1016/S0020--7373(80)80016--2 · DOI
  6. 10.1037/10903-000 · DOI
  7. Stephen Few. 2004. Show me the numbers: Designing Tables and Graphs to Enlighten. Analytics Press, Burlingame, CA, USA.
  8. 10.1186/s41077-0180086--1 · DOI
  9. Noah Iliinsky and Julie Steele. 2011. Designing data visualizations: representing informational relationships. O'Reilly Media, Inc., Sebastopol, CA, USA.
  10. 10.1016/B978-0--12--802975--6.00002--8 · DOI
  11. 10.1097/00024776--200803000-00009 · DOI
  12. 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.
  13. 10.1002/9781119055259 · DOI · Paper page
  14. 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.
  15. 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.
  16. 10.1007/978--3-030--218140_8 · DOI
  17. Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos and Sheelagh Carpendale. 2018. Data-driven storytelling. CRC Press, Boca Raton, FL, USA.
  18. Puripant Ruchikachorn. 2016. Integrating Visualizations into the Authoring of Narratives. Ph.D Dissertation Dissertation State University of New York at Stony Brook NY USA.
  19. 10.1007/s11412-014--9202-y · DOI
  20. 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.
  21. 10.1109/TLT.2016.2599522 · DOI
  22. 10.1007/s40593017-0146-z · DOI
  23. 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.
  24. 10.1007/9783--319--67925--9 · DOI
  25. 10.1007/s40593-017-0160--1 · DOI
  26. 10.1007/978--3--319--266336_3 · DOI

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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
DOI
10.1145/3313831.3376148
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
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
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

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