Advancing tertiary statistics education: A threshold framework and predictive modelling for enhancing student success and achieving consistent fundamental learning

Advancing tertiary statistics education: A threshold framework and predictive modelling for enhancing student success and achieving consistent fundamental learning

Teaching statistics has become increasingly complex as educators strive to engage diverse learners while addressing persistent gaps in mathematical confidence and understanding. Educators also face the additional challenge of effectively teaching statistics to online students who are often studying part-time and are geographically isolated from their fellow students and educators. This thesis by publication addresses these challenges through three interrelated studies, each focusing on various aspects of curriculum design, assessment, and predictive modelling to support student success in studying statistics. The first research publication investigated the challenges faced by students achieving a Pass-grade (50-64%) in a large first-year introductory tertiary statistics course through the use of innovative techniques such as consistency of learning, combination analysis, and heatmaps to evaluate performance across learning modules, identifying problems with fragmented student learning of core statistical knowledge. These novel evaluation approaches provided a clearer picture of student knowledge upon course completion. The second research publication introduced the Threshold/Expanded Competencies Curriculum (TECC) Framework, developed to address curriculum design concerns to improve student fragmented knowledge, engagement, motivation, progression, and overall success. The third research publication focused on the application of predictive modelling to examine engagement and success of online statistics education, identifying at-risk students early in the teaching period. Overall, this complete study has established a comprehensive approach to enhancing student engagement and performance in introductory statistics courses. By addressing the variability in student knowledge when achieving a Pass-grade, developing a robust curriculum and assessment framework, and leveraging predictive modelling, the research provides practical recommendations for educators and Higher Education Institutions. These insights are crucial for improving the quality and effectiveness of teaching practices in online tertiary statistics education, ultimately fostering student success by ensuring a cohesive understanding of foundational statistical concepts and providing both students and educators with a clear grasp of the knowledge acquired by students upon course completion.

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