Primary supervisor
Roberto Martinez-MaldonadoResearch area
Human-Centred ComputingI am seeking PhD candidates interested in working on designing Learning Analytics or similar reflection interfaces that automatically highlight design elements of data visualisations and generate narrative to communicate insights (instead of just plotting data).
The aim for this PhD is to research, prototype and evaluate approaches to increase the explanatory effectiveness of the visualisations contained in learning analytics or similar support 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 learning analytics tools are not designed as explanatory interfaces. This is an area that can lead to important contributions in the areas of learning analytics and information visualisation.
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 learning theories, heuristics or learning design aspects drive the design of explanatory visualisations?
- How can teachers or facilitators 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,”?
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
Skills and dispositions required:
- A Masters degree, Honours distinction or equivalent with at least above-average grades in computer science, mathematics, statistics, or equivalent
- 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.)
- Experience with data mining, data analytics or business intelligence tools (e.g. Weka, ProM, RapidMiner). Visualisation tools are a bonus.
It is advantageous if you can evidence:
- Experience in designing and conducting quantitative, qualitative or mixed-method studies
- Familiarity with educational theory, instructional design, learning sciences or human-computer interaction/CSCW
- Peer-reviewed publications
- A digital scholarship profile
- Design of user-centred software
