Visualising and evaluating learning/achievement consistency in introductory statistics

Abstract

In tertiary education, assessment plays a critical role in shaping student engagement and measuring learning outcomes. In introductory statistics courses, understanding earlier material is essential for later topics, necessitating consistent engagement to avoid fragmented learning. Assessment influences motivation and the depth of conceptual understanding upon course completion. Traditional methods such as cumulative grading and learning analytics often fail to capture the complexity of student knowledge. This research employed a multi-layered approach, including innovative ‘consistency of learning’, ‘combination analysis’ and ‘heatmap’ techniques, to examine performance across 11 learning modules. Results showed that Pass-grade (50–64%) students often did not complete key modules adequately, resulting in fragmented understanding. The study highlighted the limitations of traditional evaluation methods in capturing the complexity and variability of student knowledge. It further emphasized the importance of thoughtful assessment design to ensure that students developed a cohesive understanding of the material regardless of the grade level they achieve. Given the increasing importance of statistical literacy in today’s data-centric society, it is vital to equip students with the knowledge to make informed data decisions. By integrating these novel evaluation methods, educators can better understand and support student achievement and improve learning outcomes in introductory statistics.