The main conference will run on Thursday 3rd and Friday 4th September, details below.
This year the sigma-Network is hosting a workshop day on Wednesday 2nd September, at Heriot-Watt University, for sigma members (note it is free to become a member). Workshop details can be found here.
Conference Themes
- Assessment in Mathematical Sciences
- Teaching and Supporting Statistics
- Globalisation (e.g. joint programmes, supporting international students, global teaching, decolonisation)
- Teaching and Support with Gen-AI
- Developing Industrial Partnerships and Graduate Attributes
Keynote Speakers
Don Shearman
Bio: Don Shearman is a mathematics lecturer within the School of Mathematics and Statistics at the University of New South Wales. His career in secondary and higher education spans more than four decades, with a research portfolio dedicated to mathematics education. Key areas of his expertise include university transition, diagnostic assessment, and how success is influenced by mathematics and statistics support.
At UNSW, Don currently spearheads the creation of computer-adaptive diagnostic testing to assist with the placement of first-year students and the development of associated learning resources. His active research also explores the effectiveness of proctored online exams and patterns in student academic progression. Previously, he was a founding contributor to the University of Western Sydney’s Mathematics Education Support Hub, an acclaimed national initiative providing undergraduate academic aid.
In 2025, Don became the first international awardee of the Lawson-Croft Award, recognized by the sigma network for excellence in mathematics and statistics support. Additionally, he serves on the steering committee for FYiMaths, a prominent Australian network for mathematics and statistics support providers and educators.
Title: Diagnose. Support. Succeed.
Abstract: Universities face the challenge of supporting increasingly diverse cohorts of students entering disciplines with widely varying levels of assumed knowledge in mathematics. Traditional diagnostic approaches aim to identify weaknesses but provide limited pathways for meaningful remediation at scale. This talk explores an integrated model in which adaptive diagnostic testing is combined with targeted self-paced learning resources to support students’ transition into tertiary mathematics.
The presentation will examine how adaptive diagnostic tests can efficiently identify individual students’ strengths and gaps across prerequisite mathematical concepts while reducing the testing burden on students through personalised question pathways. Particular attention will be given to the design of knowledge maps, the interpretation of diagnostic data, and the ways in which such processes can provide actionable information for both students and educators.
The talk will then consider how identified gaps can be addressed through aligned self-paced resources that encourage students to revisit foundational concepts, practise skills, and monitor their own progress. By linking diagnostics directly to tailored learning pathways, institutions can move beyond simple classification of students toward a developmental model that promotes mathematical readiness, confidence, and agency.
Drawing on practical examples and emerging approaches in mathematics support, the session will discuss issues of scalability, student engagement, validation of diagnostic instruments, and the role of adaptive systems in contemporary mathematics education. The talk will also reflect on how these approaches may evolve in an educational landscape increasingly influenced by artificial intelligence.
The talk aims to stimulate discussion about how mathematics educators and support practitioners can create more responsive, evidence-based systems that help students successfully bridge the gap between assumed and actual mathematical knowledge.
Paola Iannone
Bio: Paola Iannone is Professor of Mathematics Education in the School of Mathematics at the University of Edinburgh and the Head of the The Technology Enhanced Mathematical Sciences Education Theme. Paola completed a first degree in mathematics at the University degli Studi di Roma La Sapienza (Rome – Italy) and a PhD in Pure Mathematics the University of East Anglia (UEA). Since her PhD she has worked in the Schools of Education at UEA and in the Department of Mathematics Education at Loughborough University. She took up her post at the University of Edinburgh in May 2023. She has established and chairs the European conference on feedback and assessment in mathematics FAME which is now in the second iteration (https://fame2.renyi.hu) and is Editor in Chief of the International Journal of Research in Undergraduate Mathematics Education (https://link.springer.com/journal/40753).
Her research focuses on the teaching and learning of mathematics at university level and has three strands: summative assessment of mathematics at university; mathematical reasoning and technology in university mathematics (use of automated theorem provers – CAA assessment) and The transition from school to university mathematics.
Title: Research and scholarship working together in mathematics education: the example of how mathematics education research findings can be embedded in course design .
Abstract: A short survey published on the LMS Newsletter in 2025 found that about 13% of the academic staff in mathematics departments in the UK are on what are often called ‘teaching and scholarship’ contracts – namely contracts where the bulk of the workload is allocated to teaching, but that contain a component of what his called ‘scholarship’. However scholarship is not a well defined entity and often colleagues are unsure of what activities may be encompassed in this term. In this talk I will first reflect on the relationship between educational research and scholarship in mathematics education. I will then illustrate how scholarships can, for example, be understood as (educational) research based teaching and course design. To illustrate this I will give the example of the course ‘Introduction to University Mathematics’ at the University of Edinburgh and explain how its design is based on current educational research, as well as on the extended professional experience of colleagues who designed it. This course addressed students’ difficulties in the transition to university mathematics, the difficulties with proof and the need for assessment variety at the university level.
Jennifer Gaskell
Bio: Dr Jenn Gaskell is a Lecturer in Statistics & Data Analytics at the University of Glasgow and an AI Lead for the Academy of Mathematical Sciences. Her scholarship works focuses on curriculum change and novel assessments, particularly in programming courses, in the age of AI. She enjoys teaching effective and responsible AI usage and has developed several training courses around this.
Title: AI and the Future of Statistics Education: What Should We Teach and How Should We Assess It?
Abstract: Generative AI is transforming how students learn and practice statistics and the required skills for graduates are rapidly evolving. Tools that can write code, run analyses, and interpret results challenge traditional approaches to teaching and assessment. Can this treat to academic integrity be harnessed as a great opportunity for curriculum review?
This talk explores how AI is reshaping statistics education, with a focus on implications for curriculum design, learning outcomes and assessment. It considers how we might adapt what we teach and how we assess in order to emphasise statistical reasoning, critical thinking, and effective use of modern analytical tools.