Exploratory versus explanatory visual learning analytics: Driving teachers’ attention through educational data storytelling
Open accessAbstract
From a human-centred computing perspective, supporting the interpretation of educational dashboards and visualizations by the people intended to use them exposes critical design challenges that may often be trivialized. Empirical evidence already shows that “usable” visualizations are not necessarily effective from an educational perspective. Since an educator’s interpretation of visualized data is essentially the construction of a narrative about student progress, in this paper, we propose the concept of “Educational Data Storytelling” as an approach for explaining student data by aligning educational visualizations with the intended learning design. We present a pilot study that explores the effectiveness of these data storytelling elements based on educator responses to prototypes by analyzing the kinds of stories they articulate, their eye-tracking behaviour, and their preferences after inspecting a series of student data visualizations. The dual purpose is to understand the contribution of each visual element for data storytelling, as well as the effectiveness of the enhancements when combined.
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 Echeverria, Martinez-Maldonado, Granda et al., 2018
Manual scaffolding often depends on direct, time-intensive intervention by educators, who may not always be available to provide ongoing guidance to every student.
“For instance, manual scaffolding often depends on direct, time-intensive intervention by educators, who may not always be available to provide ongoing guidance to every student (Echeverria, Martinez-Maldonado, Granda 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 Echeverria, Martinez-Maldonado, Granda et al., 2018
GenAI agents can infuse interactivity into VLAs, transitioning from exploratory pattern identification to providing deeper, explanatory insights.
“These agents can infuse interactivity into VLAs, transitioning from exploratory pattern identification to providing deeper, explanatory insights (Echeverria, Martinez-Maldonado, Shum et al., 2018; Ooi et al., 2023).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Echeverria, Martinez-Maldonado, Shum et al., 2018
Data storytelling is an effective scaffolding technique to enhance individuals' comprehension of complex VLA.
“Data storytelling has emerged as an effective scaffolding technique to enhance individuals' comprehension of complex VLA (Echeverria, Martinez-Maldonado, Shum et al., 2018; Pozdniakov et al., 2023).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Echeverria, Martinez-Maldonado, Shum et al., 2018
Cited by
Sorted by citations
Human-centred learning analytics and AI in education: A systematic literature review
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100215 · Cited by 281 · see citing works
A Conceptual Framework for Human–AI Hybrid Adaptivity in Education
Holstein, K., Aleven, V., & Rummel, N. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-52237-7_20 · Cited by 168 · 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
Educator perspectives on learning analytics in classroom practice
Shibani, A., Knight, S., & Buckingham Shum, S. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100730 · Cited by 86 · see citing works
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-citationThe TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms
An, P., Holstein, K., d'Anjou, B., Eggen, B., & Bakker, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376277 · Cited by 69 · see citing works
“Yet at present, the attentional continuum [14, 16] is rarely an explicit consideration in the design of teacher-facing classroom technologies [9, 10, 34].”
BackgroundMy teammate is AI: understanding students’ perceptions of student-AI collaboration in drawing tasks
Kim, J., & Cho, Y. H. (2025) · Asia Pacific Journal of Education · doi:10.1080/02188791.2023.2286206 · Cited by 50 · see citing works
Pozdniakov, 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
- Authors
- Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., & Conati, C.
- Affiliations
- Escuela Superior Politecnica del Litoral (EC)
- University of British Columbia (CA)
- Venue
- Journal of Learning Analytics
- Published
- 2018-11-24
- Volume
- 5
- Issue
- 3
- ISSN
- 1929-7750 (ISSN-L)
- OpenAlex
- W2904994877
- Cited by
- 70
- References
- 0
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 19 · 2023: 10 · 2022: 5 · 2021: 10 · 2020: 8 · 2019: 3
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Learning Analytics (publishedVersion, cc-by-nc-nd)
- Cosmopolitan Civil Societies: An Interdisciplinary Journal (University of Technology Sydney) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- educational dashboards, teacher attention, eye tracking, visual elements
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
