Learning analytics to support lifelong learning – Recognition of Prior Learning for Short Form Credentials
Dr Aneesha Bakharia (Institute for Teaching and Learning Innovation) and Associate Professor Kirsty Kitto (University of Technology Sydney) present a webinar titled Learning analytics to support lifelong learning – Recognition of Prior Learning for Short Form Credentials.
Abstract
People increasingly dip in and out of higher education over a lifetime. They need to upskill and retrain for jobs that did not even exist when they first left the education sector, and sometimes they need to move into new sectors as the job they trained for disappears due to emerging technologies. Attempting to address this problem, many Australian universities are moving towards more porous models of learning by disaggregating their courses to facilitate more flexible models of lifelong learning. Microcredentials, stackable degrees, and short forms of learning are starting to proliferate, but how will learners navigate through this far more flexible delivery model? We need ways to support the recognition of prior learning, skills profiles that evolve over a lifetime of learning, and the delivery of recommendations about what someone could study to achieve identified career goals. This talk will cover some of the research that is underway at the University of Technology Sydney in this space.
About the speaker
Associate Professor Kirsty Kitto (University of Technology Sydney) works to understand and model complex cognitive processes in a real-world context. Originally trained in theoretical physics and computer science, she has since worked in cognitive science, information retrieval, and now focusses upon learning analytics. She has led an OLT-funded project on Learning Analytics beyond the LMS (see www.beyondlms.org) and is an active member of the Society for Learning Analytics Research (SoLAR). At UTS she is based in the Connected Intelligence Centre (UTS:CIC), which takes a human-centred approach to the design and delivery of learning analytics solutions that will support student learning.
About Learning Analytics and Digital Learning Community of Practice
All UQ staff are invited to attend these sessions aiming to develop a community of practice around learning analytics, evidence-based teaching practice and digital learning.
Each session intends to cater to a variety of presentation and participation formats including presentions, lightning talks, practical data processing tutorials, predictive model building tutorials, learning tool design sessions, recent publication/conference overviews, as well as panel discussions and debates.
For general enquiries please contact learninganalytics@uq.edu.au.