Centre for Applied Research in engineering and computing Education (CARE) envisions a future where the University of Sydney Faculty of Engineering is a world-leader in personalised learning at scale. We are internationally recognised for developing innovative approaches that are grounded in, and contribute to, educational scholarship, and which enable educators to focus on individual student needs while maintaining scalability and efficiency.

Our approaches will be known for leveraging advanced learning analytics, artificial intelligence (AI)-driven adaptive systems, and evidence-based pedagogical strategies, to empower our educators to deliver customised learning experiences that enhance student engagement, retention, and skill development. 

The Centre for Applied Research in engineering and computing Education (CARE) at the University of Sydney is committed to driving rapid, research-informed improvements in engineering and computing education. We focus on solving the Faculty’s most pressing educational challenges, ensuring strong student learning outcomes while maintaining a high-quality student experience. Our work is anchored in rigorous scholarship, prioritising initiatives that deliver meaningful and scalable impact.   

CARE serves as a central hub for educational research, innovation, and evaluation, fostering a scholarly community of practice for academic and professional staff. We actively build capacity and capability, particularly among education-focused staff, while supporting all who are committed to advancing teaching and learning. Through applied research, we identify and address barriers to excellence in educating the next generation of engineers and computing professionals. 

We leverage existing scholarly practices in educational innovation and lead evidence-based approaches for communicating, engaging with, and motivating students. By collaborating across disciplines and engaging with global best practices, CARE ensures that the Faculty remains at the forefront of engineering education. We take leadership in sharing our insights internationally, shaping the future of personalised learning at scale and strengthening the University’s global impact in education. 

The Faculty of Engineering has recognised the need to significantly enhance the quality of its educational offerings, and has built this into a range of initiatives within its new strategic plan.

One of these initiatives – the development of this Centre – will focus in two key areas:

1. Capability and capacity building related to Scholarship of Teaching and Learning (SOTL); and
2. Key projects that target a scholarly approach to addressing significant educational challenges identified by the Faculty. 

A core element in achieving high learning outcomes and an outstanding student experience is having every student feel individually supported. Whilst many institutions have achieved this with small numbers of students, a key challenge is to understand how this can be achieved at scale. Addressing and leading in this challenge is core to the Faculty’s educational strategy.

Within this context, the Centre will: 

• Support development, piloting and evaluation of ideas which will drive excellence in curriculum and teaching practices, deliver learning experience tailored to individual student needs and maximise learning outcomes; 
• Build a scholarly community of practice for academic and professional staff and be a central hub for educational research, innovation and evaluation, with a strong foundation on rigorous scholarship; 
• Provide mentoring and career development for all staff, though with a specific focus on our Education Focused staff; 
• Engage students, industry and external stakeholders in co-designing educational innovation; 
• Leverage existing educational innovation expertise across the University 
• Through research, understand current challenges impeding excellence in educating the engineering and computing graduates of the future; 
• Lead evidence-based approaches for how we communicate, engage with, and motivate our students; and, 
• Take leadership in promoting and sharing our best practices globally, and engaging internationally to ensure we are connected with and learning from best practices elsewhere.  

Our actions and outputs will be underpinned by evidence and their impact will be tracked and monitored over time. We will embrace calculated risks in exploring educational innovations and navigate setbacks with support from our leaders. We will evaluate our progress and learn from our mistakes. As a centre for experimentation, innovation and shared learning, the centre will support continuity and sustainability of innovations and drive adoption of innovation across the faculty. 

A key challenge for many institutions – especially where we have large cohorts – is coping with the diverse backgrounds, motivations, and capabilities of students. We have previously successfully trialled an assessment model that explicitly differentiates between the achievement of core learning outcomes (which lead to a passing grade) and optional advanced outcomes (which lead to higher grades). This project involves investigating student reactions to this model, and how it can be adapted to different contexts.

Learning activities almost always involve students who are at a range of levels, and yet the activities will typically be common across students in the class. This can mean that those activities might be misaligned with the needs of individual students.

This project is investigating ways in which we can most effectively and efficiently respond to this challenge within classroom settings, adapting activities to suit different student needs.

Generative AI clearly represents both a major challenge and an enormous opportunity for educators. This project is focused on exploring ways in which we might be able to utilise genAI to enhance our provision of constructive and highly customised feedback to students. This is particularly significant in the context of how we cope with a large diverse student cohort, ensuring that each student receives support that is relevant to their needs. 

Rather than simply using genAI as a tool for generating templated feedback, we are particularly interested in whether genAI can be used to identify primary patterns in student submissions, so that a feedback rubric can be co-constructed with educators in a tight feedback generation loop.

Many institutions are finding that attendance by students in face-to-face classroom sessions has been declining significantly. This project is investigating the underlying drivers for poor attendance, and how this is impacting on their learning (and learning strategies).

A major challenge in many large courses is the high volume of students who are unaware of key elements or requirements of the unit. This is often related to a breakdown in the communication channels (often interpreted as students not reading outlines, announcements etc.). This may, in turn, be related to the range of the channels used (emails, canvas, Ed, …), the difference from their normal channels (social media, etc.), and the volume and complexity of the messaging.

This project is investigating the nature of student engagement with our communications to them, and why many students appear to not be familiar with unit requirements and other information already communicated to them. It will use this to develop alternative mechanisms to address these challenges with student communications, and explore the success (or otherwise) of these mechanisms.

Students often spend much more time interacting directly with their TAs/tutors (and other learning support staff, such as lab assistants) than they do with course coordinators / lecturers. As such, the quality of our education support staff is a key driver of the student experience. Whilst most institutions have mechanisms for selecting and training our TAs/tutors, we often have poor mechanisms for evaluating the effectiveness of these staff.  

This project is exploring best practice in this area and aims to develop a set of mechanisms for evaluating our tutors and using these evaluations to guide tutor selection and support.

Student timetables are one key driver of the overall experience for students, and yet we typically construct timetables based on numerous constraints other than student learning (room availability, staff preferences, etc).  

This project is exploring the factors that lead to timetables that are unfriendly for students and ways in which these issues can be addressed.

Many institutions implement caps on enrolments – either limiting the numbers who can enrol in electives and using mechanisms such as waiting lists; or utilising multiple "sections" (each taught independently) with a maximum enrolment in each section. This project will investigate the suitability and impact of these mechanisms.