Does digital assessment (DA) enhance learning and learner success, or merely prioritise efficiency and novelty? Drawing on a scoping review of 64 studies, this workshop explores the tension between automation and the need for meaningful mathematical communication.
Kaitlin Riegel begins with an interactive “Micro-Experience,” simulating how students may experience DAs. Using these lived experiences as a live dataset, she interrogates how DA influences students, with particular attention given to equity, engagement, and attitude. In small groups, participants then redesign a standard digital assessment task, applying evidence-based strategies to restore the “heart of learning”.
Bridge the gap between technical scalability and pedagogical depth. Leave with a toolkit to ensure that, even in a digital environment, assessment serves the learner, not just the system.
Kaitlin Riegel is a Postdoctoral Research Fellow at the University of Auckland, specialising in how assessment design shapes student learning and affective experiences. Her current work leverages generative AI to explore Explain in Plain Language (EiPL) problems in tertiary computing education. A PhD graduate in Mathematics Education, Kaitlin’s research focuses on the intersection of self-efficacy, achievement emotions, and assessment format. She developed the Measure of Assessment Self-Efficacy (MASE) to capture how students navigate diverse testing environments. Committed to evidence-based STEM education, Kaitlin aims to improve assessment validity and equity by recognising that student beliefs and emotions are central, not peripheral, to academic success.
Find Kaitlin’s presentation slides here: Kaitlin Riegel The Pen Vs. The Pixel Designing Digital Assessment For The Human Learner
