Reflections on Designing and Running Visualization Design and Programming Activities in Courses with Many Students

TL;DR

The paper investigates how to scale data visualization design activities in large courses by detailing six two-hour exercises in a 130-student master course, guided by autoethnographic reflections. It presents three core themes—scaffolding through continuous planning, balancing homogeneous and diverse problem spaces, and communication among students, TAs, and lecturers—grounded in practical activity design and iterative course improvement. The approach demonstrates how to structure both design and programming phases (notably Altair) to support learning outcomes while acknowledging constraints of large cohorts. The findings offer actionable guidance for balancing design exploration with data wrangling needs and for organizing teaching teams to scale visualization education with transparency and community of practice.

Abstract

In this paper, we reflect on the educational challenges and research opportunities in running data visualization design activities in the context of large courses. With the increasing number and sizes of data visualization course, we need to better understand approaches to scaling our teaching efforts. We draw on experiences organizing and facilitating activities primarily based on one instance of a master's course given to about 130 students. We provide a detailed account of the course with particular focus on the purpose, structure, and outcome of six two-hour design activities. Based on this, we reflect on three aspects of the course: First, how the course scale led us to thoroughly plan, evaluate, and revise communication between students, teaching assistants, and lecturers. Second, how we designed learning scaffolds through the design activities, and the reflections we received from students on this matter. Finally, we reflect on the diversity of the students that followed the course, the visualization exercises we used, the projects they worked on, and when to key in on simple boring problems and data sets. Thus, our paper contributes with discussions about balancing topical diversity, scaling courses to many students, and problem-based learning.