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Data Storytelling

Primary supervisor

Roberto Martinez-Maldonado

The aim for this project is to research, prototype and/or evaluate approaches to increase the explanatory effectiveness of the visualisations contained in analytics dashboards or similar support data-intensive tools. Explanatory visualisations are those whose main goal is the presentation and communication of insights. By contrast, exploratory visualisations are commonly targeted at experts in data analysis in search of insights from unfamiliar datasets. The premise is that most of current analytics tools are not designed as explanatory interfaces. Here we are talking about users without data analysis training who may need to interact with data (for example, using a dashboard). This is an area that can lead to important contributions in the areas of learning analytics and information visualisation. We sit at the Centre of Learning Analytics at Monash so you may want to focus on educational contexts. But there is an option to focus on a more general context (e.g. using alternative datasets). 



      Student cohort

      Double Semester


      Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

      • How can data storytelling elements be automatically added to visualisations of human activity?
      • What is the impact of enriching data visualisations with data storytelling elements that communicate insights?
      • How can theories, heuristics or design aspects drive the design of explanatory visualisations?
      • How can users configure the messages to be communicated through explanatory visualisations?
      • How can these visualisations and their use be evaluated (e.g. using eye-tracking devices, think-aloud and other sources of evidence)?
      • What are the conceptual and pedagogical implications of guiding the user to “one learning story per visualisation,”?
      • How can eye-tracking evidence be used to evaluate the impact of data storytelling?


      The following paper can serve as an illustrative example of this strand of research:

      Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling.  JLA 2018 [PDF]

      Required knowledge

      Required knowledge

      Skills and dispositions required:

      • Analytical, creative and innovative approach to solving problems
      • Strong interest in designing and conducting quantitative, qualitative or mixed-method studies
      • Strong programming skills in at least one relevant language (e.g. C/C++, .NET, Java, Python, R, etc.)
      • Strong graphic design skills / experience
      • Experience with visualisation tools is a bonus.