A Case Study of Data Visualization and Storytelling Workshop
for Middle School Students
Puripant Ruchikachorn*

Patchar Duangklad†Thanisara Ruangdej‡

Chulalongkorn University
Punch Up

Punch UpPunch Up

Figure 1: All the visual data stories that were produced during the workshop. Some texts were in Thai.

A BSTRACTVisualization and storytelling skills can be trained during earlyschool years. We aimed to teach visualization knowledge througha workshop whose participants were 100 middle school students
across Thailand. With several tangible materials and an assigned
topic, a group of five students collected data, mostly from a primarysource, produced a visualization and gave a short presentation on
data insights. Despite different backgrounds, all groups shared similar visualization types. Concrete and abstract data representations inpictographs, bar charts, and pie charts were popular.
Index Terms: Human-centered computing—Visualization; Applied computing—Education;
1

I NTRODUCTION

Visualization knowledge in early school years is of visualization
research interest [1]. We designed a visualization workshop for
middle school students and analyzed their outputs according toprevious work on visual literacy in elementary school [2]. From
our experience, visualization and, especially, its critical validation
were new to many students and the format of a hands-on workshop
worked well to shorten their learning cycles. Some students had* e-mail: puripant@cbs.chula.ac.th and puripant@boonmeelab.com† e-mail: patchar@punchup.world‡ e-mail: thanisara@punchup.worldplanned to learn and later conduct their own workshops in their localschools. To improve the workshop, we reflected on the results andfuture directions.
2W ORKSHOPAll students were in either the first, the second, or the third yearof a middle school with a majority of third-year students. Theirages ranged from 12 to 15 and the median age was 14. Of all 100students, there were 57 female and 43 male students. Five students
represented a school and also formed a group in the workshop. 7schools were from Bangkok while the other 13 schools were fromvarious distinct provinces from all over Thailand. Each group wasa mix of students of different ages and school years but some werecomposed of either male or female students only because they werefrom single-sex schools.
Preceded by a session on internet safety, the workshop was a
half-day event in the afternoon on Saturday. There was a brief
introduction to data storytelling and how an improperly designed
visualization in the news could mislead people. The speakers werefrom local media organizations. After that, 7 topics were assigned to20 groups of 5 students to create a visual data story. We would likesome variations within the same topic but we also wanted to observe
whether there would be some similarity across different topics soeach topic was assigned to no more than three groups. All the topics
were as follows.
1. Family size and the number of brothers and sisters
2. Bedtime and wake up time
3. Favorite pizza toppings

Table 1: The summary of all visual data stories produced in theworkshop by topics and visualization types.
Topic

Pictograph12345672

11116Figure 2: A design created during workshop topic developement. Thiswas based on a mock-up dataset of bedtime and wake up time.

4. Favorite smart phone applications
5. How one traveled to the workshop location (in Bangkok)
6. Favorite or most popular song lyrics
7. Shoe types and sizes
The topics were chosen to span across interests and fit the schedule, particularly in terms of data acquisition which happened only
within the workshop. Datasets were not provided and the participants had to acquire their own data via various methods including
quick surveys and secondary data downloads from the internet. The
data were also expected to be of different types and scales such as
nominal and ordinal scales. We designed a few visualization examples while developing the topics. Although they were not shown tothe participants, similar designs as shown in Fig. 2 were anticipatedin the workshop results.
The students had approximately 60 minutes to create a visual
data story. We provided an unlimited supply of stationery materials
including pens, papers, paper clips, rubber bands, buttons, and balloons. Everything came in various colors of unexpectedly matching
schemes, probably due to the same source of industrial pigments.
After the session ended, there were group discussions for anotherhour. The students commented on their peers’ work and reflectedon their own challenges. The speakers and other professionals gave
their opinions on the work too.
3 R ESULTS AND D ISCUSSION
From Fig. 1, there were a variety of visual data stories. Some stories
consisted of multiple visualizations. Some stories had no visual
encodings; their data were simply written in big numbers of equal
size and they were not organized in any visual way.
As summarized in Table 1, pictographs and bar charts were themost popular. Pie charts came in a close second. Other visualizations,Venn diagram and word cloud, were used once. A table might not
be universally classified as a visualization but the data were, at least,structurally organized. When there was more than one visualization
in a story, a pictograph was among the visualizations.
Venn diagram was the only visualization type we did not mention
in the workshop introduction. We conjectured that the students
learned about the diagram from their class materials at school. They
might have seen one from a website or through social media as well.
Notice that both table and word cloud were only present in thetopic on song lyrics. Except the word cloud for song lyrics, none
matched the visualizations we made prior to the workshop. For instance, no visualization for the second topic used the clock metaphor
in Fig. 2. We observed that the topic of phone applications seemed
to be the hardest to visually illustrate. Only one group did it in apictograph, arguably one of less abstract visualizations.

Barchart

Piechart

Venn
diagram

1
21

1

1
1
2

1

WordcloudTable

11

1

1

2
6

5

1

The degrees of abstraction varied. Due to our workshop materials,
most visualizations were composed of tangible objects and drawings.
The visual elements in the charts and diagram were more abstract,compared with the units of a pictograph and a word cloud. In total,
the number of abstract data representations was slightly more than
the number of concrete or pictorial depictions.
4 F UTURE W ORK
The participants went on developing their own work at school. Theirown projects included visualizations on school bullying and fakenews and also visualization workshops for their classmates. We
would like to observe their workshops and take note of the similartiies and differences between our and their workshop results.
We noticed some missing elements in the submitted visual datastories. For example, there was no visual encoding of uncertainty in
any work. We can explore on the reason of these omissions.
In addition, we can focus on a pedagogical tool implementation [2], a particular visual format such as data comics [3], or a
specific group of audience [4] to improve our workshop. For a moredetailed analysis, a formal visualization literacy assessment [5, 6]
will be helpful. We speculate that students with better visualization
literacy skills are more likely to use abstract visual representations.
ACKNOWLEDGMENTS
This work was supported in part by Chulalongkorn University andPunch Up. The workshop was hosted by Total Access Communication Public Company Limited (DTAC).
R EFERENCES[1] F. Chevalier, N. Henry Riche, B. Alper, C. Plaisant, J. Boy, andN. Elmqvist, “Observations and Reflections on Visualization Literacy
in Elementary School,” IEEE Computer Graphics and Applications,vol. 38, pp. 21–29, may 2018.
[2] B. Alper, N. H. Riche, F. Chevalier, J. Boy, and M. Sezgin, “Visualization Literacy at Elementary School,” in Proceedings of the 2017 CHI
Conference on Human Factors in Computing Systems - CHI ’17, (NewYork, New York, USA), pp. 5485–5497, ACM Press, 2017.
[3] Z. Wang, H. Dingwall, and B. Bach, “Teaching Data Visualization andStorytelling with Data Comic Workshops,” in Extended Abstracts of the2019 CHI Conference on Human Factors in Computing Systems - CHI
EA ’19, (New York, New York, USA), pp. 1–9, ACM Press, 2019.
[4] K. Börner, A. Maltese, R. N. Balliet, and J. Heimlich, “Investigating
aspects of data visualization literacy using 20 information visualizations
and 273 science museum visitors,” Information Visualization, vol. 15,pp. 198–213, jul 2016.
[5] J. Boy, R. A. Rensink, E. Bertini, and J.-D. Fekete, “A Principled Way ofAssessing Visualization Literacy,” IEEE Transactions on Visualization
and Computer Graphics, vol. 20, pp. 1963–1972, dec 2014.
[6] S. Lee, S.-H. Kim, and B. C. Kwon, “VLAT: Development of a Visualization Literacy Assessment Test,” IEEE Transactions on Visualization
and Computer Graphics, vol. 23, pp. 551–560, jan 2017.