Funded PhD Opportunities in HCI and Learning Analytics @ Monash, Melbourne, Australia

Ask for scholarship opportunities and the application process by sending me an email to Roberto@MartinezMaldonado.net

Opportunity 1: Data Storytelling with learning data

I 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 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]

Opportunity 2: Multimodal Learning Analytics in the Classroom

I am seeking PhD candidates interested in working on designing and connecting Multimodal Learning Analytics solutions according to the pedagogical needs and contextual constraints of learning occurring across physical and digital spaces.

The learning analytics challenge for this PhD is to research, prototype and evaluate approaches to automatically capture traces of students’ activity, using multimodal analytics techniques to make sense of data from heterogeneous contexts. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

  • How can multimodal analytics approaches be applied to gain a holistic understanding of students’ activity in authentic learning spaces?
  • How can the insights of students’ activity in physical spaces be connected with higher-level pedagogies?
  • How can these insights promote productive behavioural change?
  • How can the teacher be supported with this information to provide informed feedback?
  • How can learners and teachers be supported with data in the classroom?
  • What are the ethical implications of rolling out analytics in the classroom?
  • How can this information support more authentic and holistic assessment?
  • What are the technical challenges that need to be overcome?
  • How do learning theories and learning design patterns map to the orchestration of such analytics tools?

I would be particularly interested in supervising students focusing on two broad scenarios:

Analytics of the classroom physical space. This would include collecting information via sensors from authentic classrooms and develop mechanisms to analyse the data and communicate insights to teachers, students and or decision makers. The following paper can serve as an illustrative example of this strand of research:

“I Spent More Time with that Team”: Making Spatial Pedagogy Visible Using Positioning Sensors. LAK 2019 [PDF]

Teamwork analytics. This would involve collecting multimodal data from collocated teamwork settings. I clear example would be teams of nurses training in simulated scenarios. Sensors such as positioning trackers, physiological wristbands, microphones and eye trackers could be used to model complex constructs based on low-level multimodal data. Here’s a paper that illustrates one potential scenario where this research could be applied:

Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. CHI 2019 [PDF]

Candidates

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