Learning should be inspiring, engaging and of practical relevance,
and foremost be a most enjoyable experience.
Learning Analytics for Individualized Student Support Actions
All the while I was creating these fantastic truly authentic learning environments that turned to be so much fun for everyone involved, the just-in-time individualized student support became permanent companion and integral part in all my courses (Progress Analytics 0.1). The net of just-in-time data collection swept ever wider and deeper and the ‘automation’ of just-in-time student feedback and supportive advise pushed me into the wonderful entrails of Learning Analytics. The Learning analytics I am interested in lives at the coalface front of direct student learning support rather than the bird’s eye institutional view. Although, what I’d really like to do is to create an easy accessible, seamless link between the two (Future Projects).
Schulte J (2015)
Gamification of learning within a problem solving context – Individualised learning, adaptive testing, confidence and proficiency self-ratings
UTS First Year Experience Forum 23 September 2015.
Pardo A, Schulte J
Scaling Instructor-driven Personal Support Actions (Workshop)
ALASI 2015: Australian Learning Analytics Summer Institute. 26 – 27 Nov 2015, Sydney.
Schulte J (2016)
Experiences with an Adaptive Learning Product – A Learning Analytics Perspective
The NSW Learning Analytics Working Group Meetup. 3 February 2016, Sydney.
Schulte J, Buckingham Shum S, Martinez Maldonado R (2017)
Large Scale Predictive Process Mining and Analytics of University Degree Coruses.
LAK17 Learning Analytics and Knowledge Conference. March 13-17 2017, Vancouver, Canada.
Scaling the Provision of Personalised Learning Support Actions to Large Student Cohorts
This project received an Australian Office for Learning & Teaching (OLT) grant award (2016-2018). Our aspirations for this project 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.
The first taste of its functionality, richness and power to reach out to students has been presented at an international learning analytics workshop at LAK17 (Canada) , Connecting Data with Student Support Actions – A hands-on Tutorial.
Visit the project’s own web site (OnTask) for more in-depth details and case studies.
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 (Jurgen Schulte, Simon Buckingham Shum, Roberto Martinez Maldonado) and UTexas (George Siemens).
Course Pathways: Making informed choices
Jurgen Schulte, Martinez-Maldonado, Simon Buckingham Shum S (2016)
Vice Chancellor Learning and Teaching Grant
This project is a UTS Learning & Teaching 2016 grant supported project. 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. This project is a collaboration with the UTS Connected Intelligence Centre. The proof of principle realisation of the statistical analysis and projection of student data is done in R. Pre-processing data mining of 3 million UTS student enrollments in over 1,200 degree programs is done with Rapid Miner and KNIME. Analytics extraction of longitudinal focused student pathways, performance indicators and visualisation is realised with R. The first public demonstration of the pathways dashboard tool is at the UTS Teaching & Learning Forum Showcase in November 2016.
The Dashboard: Informed Teaching
Jurgen Schulte, Rob Worthington, Susan Gibson, Kathie Egea, Simon Buckingham Shum, Michael Rothery
This project has finally taken off after a few years of simmering and maturing in the slow cooker. The Dashboard project is a collaboration with UTS Business Intelligence – Data Warehouse, the Institute for Interactive Media and Learning adn the Connected Intelligence Centre. The Dashboard is designed to bring student centered real-time whole course (subject) information at the fingertip of every course (subject) coordinator across the institution. Well informed teachers present well designed lectures. The project commenced in November 2015, entered beta-testing in September 2016 and is being deployed university-wide to some 900 course (subject) coordinators in early 2017. The Dashboard provides real-time and historic information on class specific demographics such as enrollment pattern, student performance, enrollment pathways and with the next upgrade UAC and UTSOnline engagement patterns.