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Analytics

At Curtin we are using big data collected from sources including websites, mobile phones, electronic card readers, social media, web-based games, and online learning experiences to help us better understand and improve the student learning experience and to personalise curriculum and support.

Advanced analytics greatly enhance Curtin’s ability to leverage all relevant information assets, empowering decision-making and stakeholders’ performance, experience and engagement across a number of business domains.

The Analytics project focused on leveraging data to provide insights into:

  • Student outcomes to improve student retention and experience;
  • Improving the access and reporting of teaching and learning measures for the Curtin Framework for Quality and Excellence in Teaching and Learning; and
  • Student learning behaviour in digital channels.
Achievements
Benefits

Achievements

  • Digital Delivery Insights – Establishment of a foundational learning analytics insight capability in Curtin Learning and Teaching to deliver actionable insights into Curtin business questions
  • Student Discovery Model 2016 – Building of internal capabilities to undertake Student Discovery Model (including knowledge, skills and technical capability to Curtin staff)
  • Student Retention Predictions – Support for the Student Retention task force by providing tools and insights to improve student retention
  • Exploratory Data Analysis of Careers, Leadership and Mining MOOCs

Benefits

The benefits realised through the Analytics project include:

  • Establishment of in-house capability to lead in creating learning and teaching insights from data. This has already resulted in expansion of analytics across the University, creation of a community of practice, and close working ties with the Office of Strategy and Planning.
  • The Student Discovery Model has been used to create actionable insights in schools, courses and units. For example an in-depth semester of research has been completed to better understand the impacts of retention strategies in the School of Psychology. The model has been updated twice and the methodology for creating clusters of student behaviors is now an in-house capability.
  • Student Retention Predictions resulted in a viable pilot project which has now closed its activity and is awaiting further development of the university’s data infrastructure to allow near-real time updating, so that students can discover how they are travelling and staff can better assist students with relevant immediate needs.
  • New data science capabilities are springing up across campus and are beginning to support each other and work together. Early indications in the Careers and Leadership programs, for example, has already shown the value of data-driven decisions making for program improvement and development.