Supportive Artificial Intelligence and Learning Analytics for students
As universities face the challenges of a growing number of student failures and fewer student completion rates, Learning Analytics (LA) is becoming more sophisticated in analysis of student behaviour and prediction of success.
The reasons students may struggle and fail are varied and hard to predict but some clever LA data collection and analysis can help simplify complex learning processes for both staff and students, address individual needs of students and provide more equitable and socially just education opportunities.
The UOW SAILAS project aims to help students who may be at risk of failing subjects, by providing them with interventions to guide and support them through their course at university. These interventions could be offered personally, as automated responses, a Moodle plug-in or even as a Chatbot user interface.
The project involves trialling a Predictive Model using existing LA and using this information to help Faculty of Business at-risk students by offering face-to-face tutoring sessions, and useful student resources.
Further data will be collected based on student behaviour and academic profiling, and feed into the work of Phase 2 of the project (2018) where a Data Pipeline will be developed to automate the Learning Analytics Students of Interest Report. Additionally a Recommender System will be developed to automatically bring students into contact with support services to be tested more widely in 2019. It is envisaged the model would offer more personalised support services to students with an automated user interface that is proactive or interactive rather than reactive (e.g. a search box).
Measures of project success include an increase in the number of students seeking help and using student support services, and some automation of more routine staff processes. The project also hopes to establish a sense of trust and belonging for students at UOW.
For staff across campus, the project will aim to encourage a better understanding of the use and importance of LA and artificial intelligence tools, discussion on good subject design, and systems to capture student engagement and behaviour.
Project Team
- Senior Lecturer in AI in Education - Dr Rory Sie ( now working at JOHAN sports in the Netherlands. Contact via:rory.sie@gmail.com )
- Manager Learning Analytics - David Fulcher
- Student Ombudsman - A/Prof Margaret Wallace
- Education Data Analyst - Dr Flynn Hill
- Programmers - Ngoc Trinh Cao, Alexander Nicholson
- Research Assistant/Student Advocacy Officer - Rachel Cathcart
- Education Resource Development Team at LTC
- Project and Communications Manager - Vicky Wallace
Collaborators and Stakeholders
Student Services, Student Transition and Success team, Student Support & Disability, IMTS/ IMU, HEPPP Advisory group, Strategic Planning, other HEPPP project teams.
For further enquiries about the SAILAS project please contact vwallace@uow.edu.au