UTS Data Science Institute works with leading technology companies and Australian schools to better understand the how computer-assisted learning environments can improve learning success for students with different learning styles and capabilities.
Education
The team draws on contemporary machine learning and data analytics approaches such as eye tracking, key board use and mouse movement, applying these types of research to assist in diagnosing students with learning challenges.
Over the 40 years since its debut, computer-aided teaching and learning has grown from novelty to near-pervasive, with a host of dynamic interactive digital systems underpinning many aspects of the modern student experience.
Yet, while potentially transformative, the wholesale integration of computing systems into contemporary schooling has not yet delivered solutions to enduring problems in the education sector:
- Though it is widely known that students learn in different ways and at different paces, it remains difficult to identify optimal engagement and content delivery systems, particularly at the individual student level.
- Learning outcome assessment still hinges on sporadic milestone events (exams, assignment delivery, etc.), rather than a deeper ongoing (real-time) assessment of learning, understanding and progress across the student education experience. The upshot is that teachers are hamstrung in identifying student learning difficulties and in the depth of information they can draw on to tune their response.
