PhD Opportunities

Data Storytelling with learning data

(more information coming soon, email me for enquiries)

Multimodal Learning Analytics in the Classroom

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?

Analytic Approaches

We are seeking a PhD candidate 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. Providing continued support in the classroom, for mobile experiences and using web-based systems has been explored to different extents and each poses its own challenges. An overarching concern is how to integrate and coordinate learning analytics in a coherent way.

Addressing these questions should lead to educationally grounded machine learning techniques that give insight into heterogeneous activity traces (e.g. Martinez-Maldonado et al, 2018), and the design and evaluation of teacher and/or student-facing dashboards that provoke productive sensemaking, and inform action (e.g. Martinez-Maldonado et al, 2012). We invite your proposals as to which techniques might be best suited to this challenge.

You will work in close collaboration with ‘clients’ from other faculties/units, and potentially industry partners. For more information about ongoing research in this area, please visit the CrossLAK website.

Examples that can help you understand the kind of research we are currently associated with this PhD topic include the following:

HealthSimLAK: Multimodal Learning Analytics meet Patient Manikins

High Performance Teamwork Analytics in Physical Spaces

Candidates

In addition to the skills and dispositions that we are seeking in all candidates, you should have:

  • 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
  • Peer-reviewed publications
  • A digital scholarship profile
  • Design of user-centred software

Interested candidates should contact Roberto.Martinez-Maldonado@uts.edu.au  with informal queries. Please follow the application procedure for the submission of your proposal.

References

Aljohani, Naif R. and Davis, Hugh C. (2012) Learning analytics in mobile and ubiquitous learning environments. In Proceedings of the 11th World Conference on Mobile and Contextual Learning: mLearn 2012, Helsinki, Finland.

Deakin Crick, R., S. Huang, A. Ahmed-Shafi and C. Goldspink (2015). Developing Resilient Agency in Learning: The Internal Structure of Learning PowerBritish Journal of Educational Studies 63(2): 121- 160.

Kitto, Kirsty, Sebastian Cross, Zak Waters, and Mandy Lupton. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. In Proceedings of the 5th International Conference on Learning Analytics And Knowledge, Poughkeepsie, New York: ACM, pp. 11-15

Martinez-Maldonado, R., Clayphan, A., Yacef, K. and Kay, J. (2015) MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroomIEEE Transactions on Learning Technologies, TLT, 8(2): 187-200

Martinez-Maldonado, R., Kay, J., Buckingham Shum, S., and Yacef, K. (2017). Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction DataHuman-Computer Interaction, HCI, In Press.

Martinez-Maldonado, R., Yacef, K., Kay, J., and Schwendimann, B. (2012) An interactive teacher’s dashboard for monitoring multiple groups in a multi-tabletop learning environment.  International Conference on Intelligent Tutoring Systems, pages 482-492.