TY - CONF
T1 - From Data to Insight: Using Contextual Scenarios to Teach Critical Thinking in Data Visualisation
AU - Roberts, Jonathan C.
AU - Butcher, Peter
AU - Ritsos, Panagiotis D.
N1 - This paper proposes a scenario-based framework for teaching data visualisation, using concise, real-world vignettes to promote critical engagement with data. Each scenario combines a narrative, dataset, and diagram to explore contextual dimensions (who, what, why, how, when) alongside questions of audience and design choices. Two cases are presented: coal combustion data, highlighting curve-fitting pitfalls, and IoT footfall monitoring, raising ethical and interpretive challenges. The approach scaffolds critical thinking, metacognition, and ethical awareness in data representation. Student feedback indicates strong satisfaction, especially valuing authentic, context-rich case studies. The authors argue the framework is easily extensible and adaptable for wider data science pedagogy.
PY - 2025/11/3
Y1 - 2025/11/3
N2 - This paper explores the use of scenario-based visualisation examples as a pedagogical strategy for teaching students the complexities of data insight, representation, and interpretation. Teaching data visualisation often involves explaining intricate issues related to data management and the challenges of presenting data meaningfully. In this work, we present a series of data-driven scenarios. These concise stories depict specific situations, and are created to help the educators highlight key concerns in data communication, such as chart selection, temporal versus categorical comparison, visual bias, and narrative framing. By grounding these examples in real-world contexts, students are encouraged to critically assess not only what the data shows, but how and why it is shown that way. The paper presents a collection of example scenarios, that educators can use for their own lessons; the work fits with a larger project on looking at critical thinking in the classroom, and developing appropriate tools. We also start to abstract principles, from our approach, so that others can develop their own scenarios for their teaching. Our approach aligns with principles of authentic and scenario-based learning, using real-world contexts to foster critical engagement with data.
AB - This paper explores the use of scenario-based visualisation examples as a pedagogical strategy for teaching students the complexities of data insight, representation, and interpretation. Teaching data visualisation often involves explaining intricate issues related to data management and the challenges of presenting data meaningfully. In this work, we present a series of data-driven scenarios. These concise stories depict specific situations, and are created to help the educators highlight key concerns in data communication, such as chart selection, temporal versus categorical comparison, visual bias, and narrative framing. By grounding these examples in real-world contexts, students are encouraged to critically assess not only what the data shows, but how and why it is shown that way. The paper presents a collection of example scenarios, that educators can use for their own lessons; the work fits with a larger project on looking at critical thinking in the classroom, and developing appropriate tools. We also start to abstract principles, from our approach, so that others can develop their own scenarios for their teaching. Our approach aligns with principles of authentic and scenario-based learning, using real-world contexts to foster critical engagement with data.
KW - Data Visualisation
KW - Visualisation pedagogy
KW - Scenario-based learning
KW - Critical thinking
KW - Data Literacy
KW - Visualisation design
KW - Visualisation education
KW - Metacognition
KW - Case-based teaching
KW - Data science education
KW - Ethics in data science
KW - Information Visualisation
KW - Scientific Visualisation
KW - Visual Analytics
UR - https://arxiv.org/abs/2508.08737
M3 - Paper
T2 - IEEE VIS Workshop on Visualization Education, Literacy, and Activities 2025
Y2 - 3 November 2025 through 3 November 2025
ER -