Factors Associated with Unit-Specific Failure in a University-Level Statistics Course

TL;DR

This study disaggregates failure factors in a university statistics course by content unit, revealing distinct challenges and predictors for student success, and emphasizes the importance of targeted interventions to improve learning outcomes.

Contribution

It introduces a unit-specific analysis of failure in statistics education, highlighting content-specific challenges and predictors, which is a novel approach compared to traditional aggregate analyses.

Findings

Students from non-engineering programs face more difficulties in concept-heavy units.

Academic stage and perceived competence influence failure risk variably across units.

Disaggregated analysis provides nuanced insights for targeted pedagogical strategies.

Abstract

This study investigates the factors associated with failure in each of the four thematic units of a General Statistics course offered at a private university in Colombia. Unlike traditional analyses that treat performance as a single outcome, this research disaggregates results by unit: Exploratory Data Analysis, Probability and Random Variables, Statistical Inference, and Linear Regression -- highlighting distinct challenges across content areas. Based on a sample of 186 undergraduate students from Engineering, Geology, and Interactive Design programs, the study combines exam performance data with self-perceived preparedness surveys to develop unit-specific logistic regression models. The findings reveal consistent structural disadvantages for students from non-engineering programs, especially in concept-heavy units such as Inference and Regression. Academic stage and perception of competence also emerged as important predictors, though their effects varied across units. The results align with prior research on statistical thinking and self-efficacy, and support the need for targeted pedagogical interventions and curricular alignment. This disaggregated approach offers a more nuanced understanding of academic vulnerability in statistics education and contributes to the design of evidence-based, context-sensitive strategies to reduce failure and improve learning outcomes.