The Learning Analytics Team at ITaLI

The Learning Analytics team at ITaLI provides support to the evaluation, enhancement and learning analytics strategic priorities within ITaLI and the wider teaching and learning communities at UQ. We collect and interpret data about teaching practices and emerging technologies:

  • To enhance teaching and learning practices
  • To anticipate future impacts and opportunities for UQ

The scope of our services includes both learning and academic analytics. The diagram below illustrates the vision of our team:

We adopt both top-down and bottom-up approaches for learning analytics.  A top-down, or system-wide, approach is based on the strategy and vision of the institution as a whole and performing analytics for entire groups (e.g., programs/courses/students, etc.), without zeroing in on specific sub-groups.  It goes hand-in-hand with Integrated Planning and Advising Services (IPAS) and would enable UQ, for example, to develop automated interventions to enhance teaching and learning practices.  A bottom-up approach, on the other hand, involves individuals (e.g. lecturers, course co-ordinators or faculty administrators) using only a subset of data to gain insight about student learning.  Findings from a bottom-up approach can be used as proof-of-concept and scaled up to the institution as a whole.

We endeavour to facilitate the collaborative use of data from existing UQ systems (e.g., Business Objects, Blackboard Pyramid and Echo360 (a lecture theatre recording system)) to provide information about teaching and learning.   Apart from bottom-up and top-down approaches, we are continuously looking for different approaches that are used internally or externally to ensure that we support teaching and learning innovations into the future.

What support is offered by the Learning Analytics Team?

Support provided includes the following, but is not limited to:

  • Collect and examine internal/external third party data to support UQ’s learning analytics strategy
  • Undertake data analysis, student learner modelling, program and curriculum learning performance analytics and data visualisation
  • Develop evidence-based recommendations and processes to support education-oriented evaluations in relation to teaching, learning and student experience
  • Extract data from disparate sources, combining, restricting, and modelling information in response to needs from learning analytics applied research activities
  • Provide information and prepare reports to meet internal/external mandates or regulatory compliances associated with teaching and Learning
  • Prepare reports and publications on topics related to learning analytics
  • Support ITaLI partnership projects which have a relevant focus on learning analytics 

Current and past Learning Analytics projects

Project Stakeholders Aims/Description
Learning Analytics Dashboard for Students BEL, ITS, Library, Advantage Office, Student Services and PIB

– to present sufficient information about the past offerings, allowing students to elect courses that best suit their needs.

– to use technology to intervene in real-time to support students in decision making.

– to present aggregated anonymized assessment outcomes, allowing students to compare themselves with their peers.

– to coach students toward more effective and efficient ways to succeed.

– to present students with insight on different available pathways to proceed after taking a course.  
Student Profile ITEE – to equip students not with just transcripts, but with rich data records that attests to their graduate attributes and employability skills.  
Curriculum and Teaching Quality and Risk Appraisal Schools and Faculties Exec and T&L staff  
vMarks: on-the-go video feedback for assessment in large courses Dr Jack Wang from  the School of Chemistry & Molecular Biosciences  
Investigate Patterns of Performance and Engagement in Large Classes eLIPSE