Co-designing writing analytics

Our cutting-edge research in education leads the way in productive and ethical use of data and technology in classrooms. Drawing from the fields of learning analytics, educational data mining, and artificial intelligence in education, the core focus of the research strand is in the integration and implementation of technology to improve teaching and learning practices.

Design for Learning

A body of our work has been on the design of learning environments to support learning, drawing on technology both in the design process, and to enhance the learning environments.

Evaluating apps and technologies for learning

A range of mobile apps exist to support teachers and students across disciplines, including in science education. Using the ipac framework and approaches from computer science a number of analyses have been conducted of such apps and their educational aspects including supported pedagogies.

Tailored Recruitment Analytics and Curriculum Knowledge (TRACK)

A new strategic project, TRACK, is using data, analytics and Artificial Intelligence to help students make good decisions to land their dream job. It’s also helping UTS design curriculum that anticipates the skills required in the workplace of the future.

Technologies for 21st Century Self-and-Peer-Assessment (REVIEW)

A significant body of centre research has focused on developing assessment strategies that support learning. These approaches have included: Development of the REVIEW software for self-assessment Creation of learning analytics tools, particularly focused on ‘professional reflection’ to support professional development Analysis of ‘benchmarking’ tasks and use of exemplars to support learning (you can read more about this project here ) Designing approaches to assessing 21st century competencies, and holistic assessment for university entry (see Darral’s UTS Social Impact case study ) The idea of building up student’s ‘evaluative judgement’ is common across these, and described in a bit more detail below.

Understanding and modelling student STEM subject choices

Work led by Tracey-Ann Palmer has investigated how students choose their subjects for their final years of school and how this impacts choice of science. This work has included novel approaches to modelling these choices, particularly using Best-Worst Scaling to understand subject selection.