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).
Authentic Learning Experiences
Authentic Learning Experiences
Jurgen Schulte (plus a large number of UTS staff supporting the realisation)
This is an ongoing effort of mine to demonstrate ways of bringing sustainable, authentic learning experiences to every classroom; authentic learning experiences that are of immediate as well as long lasting value to students during students’ learning path through university and well beyond graduation. The learning experiences that I am developing here are disciplinary vertically focused supported a broad horizontal learning component.
Education Sciences, Guest Editor
Special Issue 2018: Authentic Student Laboratory Classes in Science Education
Second year students
A recently implemented authentic learning experience is the PAM Review project, a peer-reviewed student research journal which I created in collaboration with UTS ePRESS. This is an authentic learning experience introduced into second year physics course on theoretical thermodynamics. The aim was introduce an exciting, hands-on learning experience into a standard theoretical physics course that is both highly authentic and of last value to students.
Apart from learning the fundamentals of classical and statistical thermodynamics in the context of energy production, students are expected to take on the role of real academic or industrial scientist to produce new scientific insights of high quality to be published in a peer-reviewed professional published journal before the end of the semester. Students learn the theoretical dynamics background in the lecture and apply this knowledge in their self-selected research project in the accompanying workshops.
PAM Review unique article views and downloads
PAM Review is published by UTS ePRESS, all accepted papers are fully indexed with their own DOI’s, i.e, all papers can be referenced and students may refer to them in their resume as their first recognised activity as professional physicist or engineer.
Documentary: Authentic Learning Experience – UTS learning.futures
This authentic learning experience and the PAM Review journal are enjoying a stellar performance with over 30,000 paper downloads world-wide by the end of 2017. The quality and reach experienced in this authentic learning experience is demonstrated in one of the student papers being cited in the highly recognised Nature Photonics journal (2018) (impact factor 37).
First year students
In an authentic learning experience project designed for first year engineering students, we invited industry to participate the curriculum development of a practical laboratory program for engineering students. This authentic learning experience revolutionised the way our engineering students are taught fundamental physics in a laboratory program. The approach gained international attention and has been adopted for a pilot at Beijing Institute of Technology.
Learning Analytics for Individualized Student Support Actions
All the while I was creating these fantastic truly authentic learning environments that turned out 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, actionable 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.
Pardo A, Martínez-Maldonado R, Buckingham Shum S, Simon McIntyre S, Gasevic D, Siemens G, Schulte J (2017)
Connecting Data with Student Support Actions – A hands-on Tutorial.
LAK17 Learning Analytics and Knowledge Conference.March 13-17 2017, Vancouver, Canada.
Schulte J, Buckingham Shum S, Martinez Maldonado R (2017)
Large Scale Predictive Process Mining and Analytics of University Degree Courses.
LAK17 Learning Analytics and Knowledge Conference. March 13-17 2017, Vancouver, Canada.
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 has been at the UTS Teaching & Learning Forum Showcase in November 2016. In the further development of this tools we found evidence that process mining of student course pathways and student performance rapidly degrades performance as the number of course pathway and students increase (LAK 2017). We moved away from the process mining and developed a smart large scale data filtering approach of exceptional performance which now allows us to interrogate arbitrary student course pathways in almost real-time.
Now that we have rapid access to the course pathway performances, we’ll be focusing on the dissemination of this tool. The further development in 2018 is dedicated to fully embed this tool into the university’s secure data warehouse environment, to develop user experience focused graphical interfaces and rolling the tool out for user trials.