Reflections on Teaching Data Storytelling at the Journalism School

2024-08-08

AI Summary

This study addresses persistent pedagogical challenges in journalism education—including students’ weak quantitative literacy, tensions between humanistic values and technical logic, and high domain-specific content requirements—by proposing a data narrative pedagogy tailored to journalistic practice. Grounded in reflective educational practice and course-based action research, the study integrates case-based instruction with interdisciplinary comparative analysis. It pioneers the “discipline-adaptation” principle and innovatively develops a dual-path instructional model comprising “contextualized embedding” and “narrative-source migration.” The resulting reusable teaching framework and localized lesson plans effectively bridge data visualization techniques with journalistic narrative logic. By reconciling empirical rigor with storytelling integrity, this work offers both a theoretical reference and an empirically grounded pedagogical model for data literacy education in humanities-oriented disciplines globally. (149 words)

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The integration of data visualization in journalism has catalyzed the growth of data storytelling in recent years. Today, it is increasingly common for journalism schools to incorporate data visualization into their curricula. However, the approach to teaching data visualization in journalism schools can diverge significantly from that in computer science or design schools, influenced by the varied backgrounds of students and the distinct value systems inherent to these disciplines. This paper reviews my experience and reflections on teaching data-driven storytelling in a journalism school in Shanghai, China. To begin with, I discuss three prominent characteristics of journalism education (i.e., students' lack of quantitative literacy, the tension between humanism and technocentrism, and the high requirements for content professionalism) that pose challenges for course design and teaching. Then, for each challenge, I share firsthand teaching experiences and discuss corresponding approaches for teaching, such as trying to put visualization into a news context and finding commonality between data-driven storytelling and traditional storytelling. Overall, this paper aims to provide reference and inspiration for instructors who are teaching data visualization and data-driven storytelling to students with non-technical backgrounds.

Problem

• Addressing journalism students' lack of quantitative literacy in data storytelling
• Balancing humanism and technocentrism in data visualization teaching
• Adapting data storytelling for non-technical journalism education contexts

Innovation

• Integrates visualization into news context
• Bridges data and traditional storytelling
• Tailors teaching for non-technical students