Data Storytelling Editor: A Teacher-Centred Tool for Customising Learning Analytics Dashboard Narratives
Abstract
Dashboards are increasingly used in education to provide teachers and students with insights into learning. Yet, existing dashboards are often criticised for their failure to provide the contextual information or explanations necessary to help students interpret these data. Data Storytelling (DS) is emerging as an alternative way to communicate insights providing guidance and context to facilitate students’ interpretations. However, while data stories have proven effective in prompting students’ reflections, to date, it has been necessary for researchers to craft the stories rather than enabling teachers to do this by themselves. This can make this approach more feasible and scalable while also respecting teachers’ agency. Based on the notion of DS, this paper presents a DS editor for teachers. A study was conducted in two universities to examine whether the editor could enable teachers to create stories adapted to their learning designs. Results showed that teachers appreciated how the tool enabled them to contextualise automated feedback to their teaching needs, generating data stories to support student reflection.
Integrity signals
Registry
Cites
Statistics
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?
Conventional scaffolding approaches like data storytelling remain difficult to scale and adapt to individual learning needs.
“these approaches remain difficult to scale and adapt to individual learning needs.”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Fernandez-Nieto et al., 2024
The findings provide empirical evidence to support the use of GenAI agents as viable alternatives to data storytelling for facilitating accurate and efficient comprehension.
“This provides empirical evidence to support the use of GenAI agents as viable alternatives to data storytelling for facilitating accurate and efficient comprehension, echoing recent recommendations to harness advanced AI systems for understanding data visualisations (Fernandez-Nieto et al., 2024; Li et al., 2024).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Fernandez-Nieto et al., 2024
The proactive agent can be viewed as a combination of scaffolding and the passive GenAI agent.
“As these techniques are also frequently used in the creation of data stories (Fernandez-Nieto et al., 2024; Shao et al., 2024), the proactive agent can be viewed as a combination of scaffolding (e.g., data storytelling) and the passive GenAI agent.”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Fernandez-Nieto et al., 2024
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Artificial intelligence and learning analytics in teacher education: A systematic review
Salas-Pilco, S., Xiao, K., & Hu, X. (2022) · Education Sciences · doi:10.3390/educsci12080569 · Cited by 326 · see citing works
Choi, S., Jang, Y., & Kim, H. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2049145 · Cited by 294 · see citing works
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2018.05.004 · Cited by 283 · see citing works
Learning analytics dashboards: the past, the present and the future
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020) · Proceedings of the Tenth International Conference on Learning Analytics & Knowledge · doi:10.1145/3375462.3375504 · Cited by 129 · see citing works
From Data to Insights: A Layered Storytelling Approach for Multimodal Learning Analytics
Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., & Buckingham Shum, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376148 · Cited by 123 · see citing works
Self-citationPozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gasevic, D. (2023) · LAK23: 13th International Learning Analytics and Knowledge Conference · doi:10.1145/3576050.3576063 · Cited by 44 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Yan, L., Martinez-Maldonado, R., Jin, Y., Echeverria, V., Milesi, M., Fan, J., Zhao, L., Alfredo, R., Li, X., & Gašević, D. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105322 · Cited by 85 · see citing works
Self-citation
- Authors
- Fernandez-Nieto, G. M., Martinez-Maldonado, R., Echeverria, V., Kitto, K., Gašević, D., & Buckingham Shum, S.
- Affiliations
- Monash University (AU)
- Department of Human Centred Computing, Monash University, Australia
- University of Technology Sydney (AU)
- Connected Intelligence Centre, University of Technology Sydney, Australia
- ORCID
- Venue
- Proceedings of the 14th Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2024-03-18
- Pages
- 678-689
- OpenAlex
- W4392445355
- Cited by
- 22
- References
- 7
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 4
- FWCI
- 9.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- Funders
- Australian Research Council (P210100060)
- Defense Advanced Research Projects Agency (HR0011-22-2-004)
- 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
- Health Sciences › Health Professions › Speech and Hearing › Digital Storytelling and Education
- Keywords
- data storytelling, learning analytics dashboards, student reflection, automated feedback, teacher agency
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
