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C3L is leading UniSA engagement in one of the sixteen projects awarded a total of $30 million by the Commonwealth’s Cooperative Research Centres Projects (CRC-P) Round 7 grants program. The CRC-P, focused on privacy and learning analytics, is led by Practera, a Sydney-based EdTech start-up and long-term collaborator with C3L researchers. Additional collaborators in the project include CSIRO’s Data61, global education company Navitas, Education Technology and innovation industry hub EduGrowth, and cyber security solution provider Cybermerc.

The project will develop a platform that allows educational providers to share data and algorithms while still preserving the privacy of student data records (Figure 1). This will enable education institutions and companies to better support the learning needs of students through the use of learning analytics and artificial intelligence algorithms. This will provide teachers and students with insights into what is working during learning processes and how to improve self-regulation, goal setting, and learning strategy selection. The solution will be developed and tested within Practera’s experiential learning platform which supports work-integrated learning programs, internships and skills credentialing.

Previous engagement between C3L and Practera was centred around the development of personalized, analytics-driven interventions that supported students at risk of not completing the program and evaluating the impact of these interventions. A particular focus was on understanding collaboration and cooperation in team-based projects and deploying interventions to improve students’ teamwork skills. This CRC-P engagement continues this work, with the additional element of adapting previously developed algorithms to work on privacy-enhanced datasets produced by Data61. This will involve direct collaboration between Data61 and C3L to iteratively adapt their algorithms. Key activities for C3L will be:

  • Development of personalised, analytics-driven interventions to support individual and collaborative learning and work
  • Validate baseline interventions
  • Collaborate with Data61 to adapt algorithms for privacy-preserved data
  • Validate novel algorithms against the baseline


Figure 1. Key Project deliverables


Project Partners