Reflections on Teaching Data Storytelling at the Journalism School

TL;DR

The paper investigates the challenges of teaching data storytelling in journalism education, focusing on three core characteristics of journalism pedagogy: limited quantitative literacy, tension between humanistic aims and technocentric methods, and stringent professional standards. It presents a Shanghai-based Data Analysis and Information Visualization course, detailing its structure, assessment, and reflective practices, and articulates concrete teaching approaches anchored in analogical thinking, news-context visualization, and alignment with traditional storytelling. Through case studies and curated examples, the work offers actionable guidelines for instructors teaching data-driven storytelling to non-technical students, emphasizing public-interest impact, ethical design, and cross-media dissemination. The discussion highlights implications for broader education domains, the potential for AI-assisted pedagogy, and the need to tailor visualization education to diverse cultural and institutional contexts.

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.

Figures (6)

• Figure 1: Examples of data stories created by students. (a) Title: "Problems Encountered by Young People Living Alone." The bubble chart below summarizes the frequency of key topics on social media, such as social barriers and difficulties in daily life. (b) Title: "Foreigners Are Not Outsiders." The map summarizes the most popular first destinations for foreigners traveling to China, with the top three being Shanghai, Beijing, and Guangzhou. (c) Title: "Bus Passenger Flow by Time Period". The students conducted on-site visits and recorded the passenger numbers at a Shanghai bus stop from 21:00 PM to 22:20 PM (represented by bars) as well as the number of people who were unable to board the last bus (represented by icons), reflecting on the inadequacy of late-night bus services for overtime workers.
• Figure 2: (a) Printing money; (b) 9 Ways to Imagine Jeff Bezos' Wealth; (c) Comparison of the time taken by Chinese boys aged 13-18 to complete a 1000-meter run from 1985 to 2014 (Title: "The Slowing Pace of Chinese Children.")
• Figure 3: (a) Nightingale's rose chart; (b) Wikileaks Afghanistan files: every IED attack, with co-ordinates; (c) The line chart reflects the proportion of the salt tax in China's total tax revenue, which keeps going done. (Title: "The salt tax is no longer profitable, and the monopoly on salt is no longer necessary." )
• Figure 4: (a) Various visualizations of US presidential election results; (b) Visualizations of China's NPC representatives.
• Figure 5: (a) What's really warming the world?; (b) Do you know how much pork we eat each year? (the Chinese phrase in the first picture means "10 tons of pork")