Early Assessment in First-Year Transition: Predictor of Success or Catalyst for Intervention?

Authors

DOI:

https://doi.org/10.63608/ssj.3997

Keywords:

First year transition, student success, statistics education, early assessment

Abstract

Early assessment (EA) is a key component of learning, providing students with opportunities to receive feedback, clarify expectations, and build confidence. This is particularly important for first-year students transitioning to university. This study considers whether EA performance is a reliable predictor of future performance, and whether this depends on student group or EA format. Results indicate that for high-performing students, EA is a reliable predictor of future success; however, other groups have mixed results. The ability of EA to predict future performance also depends on EA format, particularly for lower-performing groups. Where EA is not a reliable predictor of future performance, we suggest that it instead, in some cases, serves as a useful catalyst, leading to improved performance. These findings highlight the importance of EA, and the careful consideration required when determining EA format and associated support strategies, to effectively scaffold student success, particularly for lower-performing and at-risk students.

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Author Biographies

Amanda Shaker, La Trobe University Australia

Amanda is a Senior Lecturer in Statistics at La Trobe University, in the Department of Mathematical and Physical Sciences. She teaches statistics to students from a diverse range of disciplines and is passionate about making statistics fun and interesting for all students. Amanda’s contributions to learning and teaching have been recognised with multiple awards, including an Australian Awards for University Teaching (AAUT) Citation for Outstanding Contribution to Student Learning in 2020, and a Vice Chancellor's Education Award in 2025. She is also a Senior Fellow of Advance HE. Amanda’s research in statistics education focusses on statistics anxiety, assessment, generative AI in statistics education, incentivised engagement, and game-based learning. Amanda is a member of the Statistical Education Section Committee of the Statistical Society of Australia.

Rupert Kuveke, La Trobe University Australia

Rupert Kuveke is a teaching-focused Lecturer in Statistics and Data Science in the Department of Mathematical and Physical Sciences at La Trobe University. He teaches Statistics and Bioinformatics at undergraduate and postgraduate levels, and is passionate about meaningful applications of Statistics for the benefit of humanity. Rupert is a Fellow of Advance HE and holds a BFin, BSc(Hons), and a PhD in Statistics.

Chris Brignell, University of Nottingham UK

Chris Brignell is Professor of Statistics and Mathematical Education at the University of Nottingham where he is Deputy Director of the Observatory for Mathematical Education, and Head of Mathematics Education and Scholarship in the School of Mathematical Sciences.  His research interests focus on national trends of participation and attainment in mathematical education, from primary schools to postgraduate courses, and pedagogical approaches and student engagement in higher education mathematics and statistics programmes.  Chris is Vice President for Public Understanding, Engagement and Education at the Royal Statistical Society.

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Published

05-11-2026

How to Cite

Shaker, A., Kuveke, R., & Brignell, C. (2026). Early Assessment in First-Year Transition: Predictor of Success or Catalyst for Intervention?. Student Success. https://doi.org/10.63608/ssj.3997

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Articles