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Cited by 44Data Visualization and AnalyticsOnline Learning and AnalyticsVisual and Cognitive Learning Processes

How Do Teachers Use Dashboards Enhanced with Data Storytelling Elements According to their Data Visualisation Literacy Skills?

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Pozdniakov, Stanislav, Martinez-Maldonado, Roberto, Tsai, Yi-Shan, Echeverria, Vanessa, Srivastava, Namrata, Gasevic, Dragan·2023·LAK23: 13th International Learning Analytics and Knowledge Conference
DOI 10.1145/3576050.3576063
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Abstract

There is a proliferation of learning analytics (LA) dashboards aimed at supporting teachers. Yet, teachers still find it challenging to make sense of LA dashboards, thereby making informed decisions. Two main strategies to address this are emerging: i) upskilling teachers’ data literacy; ii) improving the explanatory design features of current dashboards (e.g., adding visual cues or text) to minimise the skills required by teachers to effectively use dashboards. While each approach has its own trade-offs, no previous work has explored the interplay between the dashboard design and such "data skills". In this paper, we explore how teachers with varying visualisation literacy (VL) skills use LA dashboards enhanced with (explanatory) data storytelling elements. We conducted a quasi-experimental study with 23 teachers of varied VL inspecting two versions of an authentic multichannel dashboard enhanced with data storytelling elements. We used an eye-tracking device while teachers inspected the students’ data captured from Zoom and Google Docs, followed by interviews. Results suggest that high VL teachers adopted complex exploratory strategies and were more sensitive to subtle inconsistencies in the design; while low VL teachers benefited the most from more explicit data storytelling guidance such as accompanying complex graphs with narrative and semantic colour encoding.

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  • The lack of significant interactional effects between visualisation literacy and intervention conditions aligns with Shao et al. (2024)'s findings, suggesting that visualisation literacy may not be as pivotal as previously thought in determining the effectiveness of supportive interventions.

    “The lack of significant interactional effects between visualisation literacy and intervention conditions aligns with Shao et al. (2024)'s findings, suggesting that visualisation literacy may not be as pivotal as previously thought in determining the effectiveness of supportive interventions (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 Pozdniakov et al., 2023

    Split into 2 atoms — 0 confirmed, 1 partially supported, 1 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

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  • Many students experience difficulties comprehending complex visual learning analytics because of limited data visualisation literacy.

    “Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy.”

    From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Pozdniakov et al., 2023

    The Abstract and §5.1 report that teachers with lower visualisation literacy had more difficulty interpreting learning-analytics dashboards and benefited from explicit guidance. The study concerns teachers inspecting students’ data, not students interpreting dashboards; it therefore does not support the claim about many students. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The cognitive load associated with processing dense visual analytics can overwhelm students and educators, making it difficult to extract meaningful insights.

    “Moreover, the cognitive load associated with processing dense visual analytics can overwhelm students and educators, making it difficult to extract meaningful insights (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 Pozdniakov et al., 2023

    The 'Storytelling in Education' section says interpreting data visualisations involves complexity and cognitive load, and the Introduction says teachers can struggle to make sense of dashboards. The study concerns teachers interpreting student data, not students interpreting dashboards; it also does not establish that dense visual analytics overwhelm either group. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Individuals with lower visualisation literacy benefit most from data storytelling, as it reduces cognitive load and helps bridge interpretation gaps.

    “For instance, Pozdniakov et al. (2023) found that individuals with lower visualisation literacy benefit most from data storytelling, as it reduces cognitive load and helps bridge interpretation gaps.”

    From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Pozdniakov et al., 2023

    The Conclusion reports that teachers with low visualisation literacy benefited most from explicit data storytelling guidance. The Storytelling in Education section describes such guidance as aimed at reducing cognitive load and narrowing knowledge gaps, but the passages do not show that this study measured cognitive-load reduction or established it as the reason for the benefit. The finding is also about teachers using dashboards, rather than individuals generally. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • 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 Pozdniakov et al., 2023

    The Results (Section 4.1.4) and Conclusion (Section 6) report that teachers with low visualisation literacy benefited from explicit data-storytelling guidance when interpreting dashboards. The claim extends that finding to individuals generally and presents data storytelling as broadly effective, whereas the paper stresses that its benefit depends on the design and the teachers’ visualisation literacy. (LLM, conf=0.86)

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Cites

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1 of the 48 references couldn’t be matched to a known paper.

Research works printed but not linked: 6

  1. R. J. Feise. 2002. Do multiple outcome measures require p-value adjustment? BMC medical research methodology 2, 1 (2002), 1-4.
  2. E. B. Mandinach and L. M. Abrams. [n. d.]. Data Literacy and Learning Analytics. In The Handbook of Learning Analytics (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan GaÅ¡eviÄ ‡, and Agathe Merceron (Eds.). SoLAR, 196-204. https://www.solaresearch.org/publications/hla-22/hla22-chapter19/ Section: 19.
  3. A. Olsen. 2012. The Tobii I-VT fixation filter. Tobii Technology 21 (2012), 4-19.
  4. S. Pozdniakov, R. Martinez-Maldonado, et al. 2022. The Question-Driven Dash- board: How Can We Design Analytics Interfaces Aligned to Teachers' Inquiry?. In LAK22: 12th International Learning Analytics and Knowledge Conference (On- line, USA) (LAK22). Association for Computing Machinery, New York, NY, USA, 175-185. https://doi.org/10.1145/3506860.3506885 · DOI
  5. J. Saldaña. 2015. The Coding Manual for Qualitative Researchers. SAGE. https: //doi.org/10.46743/2160-3715/2009.2856 · DOI
  6. H.-J. Schulz, M. Streit, et al. 2013. Towards a characterization of guidance in visualization. In Poster at IEEE Conference on Information Visualization (InfoVis).

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Authors
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gasevic, D.
Affiliations
  • Monash University (AU)
  • Monash University, Australia
ORCID
Venue
LAK23: 13th International Learning Analytics and Knowledge Conference
Publisher
ACM
Published
2023-03-13
Pages
89-99
DOI
10.1145/3576050.3576063
OpenAlex
W4321441989
Cited by
44
References
48
Citations by year
2026: 10 · 2025: 17 · 2024: 11 · 2023: 6
FWCI
4.71
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
  • HAL (submittedVersion)
Funders
Jacobs Foundation
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
learning analytics dashboards, data storytelling, teacher data literacy, dashboard design, eye tracking, teacher decision-making, online learning data

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