Whole System Approach to Curriculum Development
This project proposes an approach to Learning Experience (LX) Design that goes beyond the design of learning content and learning pedagogy. It takes into account the special purpose-built infrastructure, quantification of learning outcomes (learning objectives,graduate attributes, course learning outcomes) and periods of on- and off campy teaching. The LX Design takes a connected parallel layer approach with verification (quantification) at each steps, thus providing a quantitative map of the design and a quatitative verification of its intended aim.
Authentic Learning in Large Engineering and
Physical Sciences Laboratories
This project is part of a Senior Visiting Research Scholarship I was awarded under the Key Technology Partnership program with Beijing Institute of Technology (P.R. China), which commenced in December 2016. The project is in collaboration with Profs Qingfan Shi and Wei Liu of the School of Physics at Beijing Institute of Technology and focuses on the teaching practices in large first year laboratory classes (>2000 students) and authentic learning in practical education. I developed an authentic learning laboratory class which was trialed in late 2017 at BIT in their physics for engineers laboratory program with exceptional success. The evaluation of this trial revealed that while students at BIT are fully embracing this new to them learning experience, for this approach to be fully adopted at BIT, substantial training for academic needs to be provided. We are currently working on exploring avenues for a larger staff training program which we’d like to implement in the near future.
Curriculum Development for Large Physical Science Courses in China taught in English Language
This project is parallel to the previous one as part of a Senior Visiting Research Scholarship I have been awarded under the Key Technology Partnership program with Beijing Institute of Technology (BIT). The project is in collaboration with Profs Qingfan Shi and Wei Liu of the School of Physics at Beijing Institute of Technology and focuses on the research and best practice informed establishment of courses in engineering and physical sciences taught in English language at BIT.
Scaling the Provision of Personalised Learning Support Actions to Large Student Cohorts
This project has been put forward to the Australian Office for Learning & Teaching (OLT). Our aspirations for this project, which has now become one of my Current Projects, are to improve the quality of student learning in large cohorts by scaling the deployment of Personal Learning Support Actions to large student cohorts within Australian Higher Education institutions. Here, Personal Learning Support Actions are any instructor led intervention that is designed to help students in their learning journey by recognising and acknowledging their strengths and weaknesses, and suggesting steps or mentorship interventions that are relevant to their particular situation. In a Higher Education context Personal Learning Support Actions encompasses a wide scope of situations including conventional actions such as the provision of feedback as well as content personalisation, advice on learning strategies, content recommendations, and visualisations. Two forks of the OnTask development have emerged, each providing for a different institutional implementation pathway. We are planning to adopt our preferred OnTask fork and shape it such that it can be fully integrated into UTS secure data environment and being made available to the larger university community as well as linked to the our Dashboard development.
The project pages at OnTask present an in-depth overview and a more current status.
This is a mutli-center multidisciplinary project spearheaded by Abelardo Pardo (USyd) with project teams at USyd (Abelardo Pardo, Kathryn Bartimote-Aufflick), UniSA (Shane Dawson, Dragan Gasevic), UNSW (Simon McIntyre, Negin Mirriahi, Lorenzo Vigentini), UTS (Simon Buckingham Shum, Roberto Martinez Maldonado, Jurgen Schulte) and UTexas (George Siemens).
Course Pathways: Making informed choices
This project has been put forward as UTS Vice Chancellor Learning & Teaching project and has now become a Current Projects. The aspiration of this project is to uncover statistically significant patterns in students’ course pathway choices with the help of data mining and with this information to derive individual student course-longitudinal ‘health’ indicators. This is done with a view to provide support units, course and subject coordinators with more longitudinal focused indicators that may be used in student personal support actions (on-demand or just-in-time individualised student support). The indicators may also help to support the streamlining of course and subject content. The more students can be informed about what it would take for them (individually) to master future subject and stages in their course, the better their study experience will be and a higher overall student retention rate may be achieved.