Analysis Without Data: Teaching Students to Tackle the VAST Challenge

TL;DR

This paper introduces a guideline to help students effectively approach the VAST Challenge by focusing on hypothesis-driven analysis, improving their ability to identify initial directions and solve complex visual analytics problems.

Contribution

It proposes a preliminary, structured guideline for students to better initiate and conduct analysis in VAST Challenges, based on a case study with two students.

Findings

Students successfully used the guideline to generate hypotheses.

The guideline helped students identify good starting points.

Students felt more confident with concrete steps in their analysis.

Abstract

The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle at identifying a good "starting point" when tackling the VAST Challenge. Consequently, students who could not identify a good starting point failed at finding the correct solution to the challenge. In this paper, we propose a preliminary guideline for helping students approach the VAST Challenge and identify initial analysis directions. We recruited two students to analyze the VAST 2017 Challenge using a hypothesis-driven approach, where they were required to pre-register their hypotheses prior to inspecting and analyzing the full dataset. From their experience, we developed a prescriptive guideline for other students to tackle VAST Challenges. In a preliminary study, we found that the students were able to use the guideline to generate well-formed hypotheses that could lead them towards solving the challenge. Additionally, the students reported that with the guideline, they felt like they had concrete steps that they could follow, thereby alleviating the burden of identifying a good starting point in their analysis process.